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67ba414 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | # DeepCoder Experiment Runs
## Command Snippets Overview
| Condition | Purpose | Variables |
|--------|---------|-----------|
| `GRPO top-p 1.0` | Full-retention baseline under linear top-p filtering | `rollout_filter_strategy=top_p`, `rollout_filter_value=1`, `rollout_filter_include_zero=True` |
| `GRPO top-p 0.9` | Stronger reward-variance filtering with adaptive retention | `rollout_filter_strategy=top_p`, `rollout_filter_value=0.9`, `rollout_filter_include_zero=False` |
| `GRPO top-k 0.25` | Fixed-budget filtering that keeps the top 25% of train groups | `rollout_filter_strategy=top_k`, `rollout_filter_value=0.25`, `rollout_filter_include_zero=True` |
All three command snippets run DeepCoder with `Qwen/Qwen2.5-Coder-7B`, `GRPO`, and single-turn code generation.
---
## 1. Top-p 1.0 (`Qwen/Qwen2.5-Coder-7B-200-GRPO-top-p-1`)
Uses linear top-p filtering with `rollout_filter_value=1`.
Goal:
- Establish a full-retention baseline while keeping the same reward-variance ranking machinery as the filtered runs
Key Details:
- Filtering uses `top_p`, `largest`, `reward_variance`, and `rollout_filter_top_p_prob_mode=linear`
- `rollout_filter_value=1` with `rollout_filter_include_zero=True` keeps the full train-group pool under linear top-p selection, so this is the closest thing to a no-filter baseline in `deepcoder_lines`
- `actor_rollout_ref.actor.use_ref=False` and `actor_rollout_ref.actor.use_kl_loss=False` remove reference-policy KL from training
- The run budget is `200` training steps with checkpoints every `20` steps
Source:
- `docs/deepcoder_lines`, lines `1-89`
Outputs:
- W&B project: `deepcoder_RAGEN_final_3`
- Run name: `Qwen/Qwen2.5-Coder-7B-200-GRPO-top-p-1`
---
## 2. Top-p 0.9 (`Qwen/Qwen2.5-Coder-7B-200-GRPO-top-p-0.9`)
Uses the same linear top-p filter, but keeps only the highest-variance groups whose score mass reaches `0.9`.
Goal:
- Increase reward-variance filtering strength while keeping the rest of the GRPO setup fixed
Key Details:
- Filtering again uses `top_p`, `largest`, `reward_variance`, and `rollout_filter_top_p_prob_mode=linear`
- `rollout_filter_value=0.9` makes retention adaptive: the number of kept groups depends on how reward variance is distributed across the `16` train groups
- `rollout_filter_include_zero=False` excludes zero-variance groups from selection
- Because `rollout_filter_type=largest`, the filter prioritizes groups with the highest within-group reward variance
Source:
- `docs/deepcoder_lines`, lines `93-181`
Outputs:
- W&B project: `deepcoder_RAGEN_final_3`
- Run name: `Qwen/Qwen2.5-Coder-7B-200-GRPO-top-p-0.9`
---
## 3. Top-k 0.25 (`Qwen/Qwen2.5-Coder-7B-200-GRPO-top-k-0.25`)
Switches from adaptive top-p filtering to fixed-fraction top-k filtering.
Goal:
- Compare adaptive top-p filtering against a fixed keep-top-25% regime
Key Details:
- `rollout_filter_strategy=top_k` with `rollout_filter_value=0.25` keeps `int(0.25 * 16) = 4` train groups per step
- With `es_manager.train.group_size=8`, this corresponds to at most `32` kept rollouts per training step after filtering
- `rollout_filter_include_zero=True` means zero-variance groups are still part of the ranking pool, but only the top `4` groups survive
- `rollout_filter_type=largest` means those `4` groups are chosen by highest reward variance
Source:
- `docs/deepcoder_lines`, lines `185-273`
Outputs:
- W&B project: `deepcoder_RAGEN_final_3`
- Run name: `Qwen/Qwen2.5-Coder-7B-200-GRPO-top-k-0.25`
---
## Common Notes
- Source format:
- `docs/deepcoder_lines` is a collection of three standalone bash snippets, not a parameterized sweep script
- The file defines both `USE_GRPO` and `USE_PPO`, but all three `python train.py` commands actually expand `$USE_GRPO`
- Shared setup across all three conditions:
- Config: `_10_deepcoder`
- Model: `Qwen/Qwen2.5-Coder-7B`
- `algorithm.adv_estimator=grpo`
- `agent_proxy.reward_normalization.method=identity`
- `trainer.total_training_steps=200`
- `ppo_mini_batch_size=32`
- `micro_batch_size_per_gpu=1`
- `es_manager.train.env_groups=16`, `es_manager.train.group_size=8`
- `es_manager.val.env_groups=256`, `es_manager.val.group_size=1`
- `trainer.n_gpus_per_node=8`
- `system.CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"`
- `actor_rollout_ref.rollout.tensor_model_parallel_size=4`
- `agent_proxy.max_turn=1`
- `actor_rollout_ref.actor.use_ref=False`
- `actor_rollout_ref.rollout.rollout_filter_type=largest`
- `actor_rollout_ref.rollout.rollout_filter_metric=reward_variance` by default from `config/base.yaml`
- `actor_rollout_ref.rollout.rollout_filter_top_p_prob_mode=linear`
- `actor_rollout_ref.rollout.rollout_filter_empty_stop_steps=0`
- `actor_rollout_ref.rollout.max_model_len=10000`
- `actor_rollout_ref.rollout.max_num_batched_tokens=10000`
- `actor_rollout_ref.rollout.response_length=4000`
- `agent_proxy.fail_on_prompt_too_long=True`
- `lora.rank=0`, `lora.alpha=64`, `lora.target_modules=all-linear`
- `actor_rollout_ref.rollout.gpu_memory_utilization=0.6`
- `trainer.save_freq=20`
- `trainer.validation_steps=1`
- `trainer.val_before_train=True`
- `trainer.test_freq=10`
- `collapse_detection.first_turn_enabled=False`
- `collapse_detection.multi_turn_enabled=False`
- `trainer.resume_mode=disable`
- Logging and artifacts:
- Default local log dir remains `results/`
- Default logger remains `['console', 'wandb']`
- Checkpoints are saved every `20` steps
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