Phi-4 Technical Report
Paper
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2412.08905
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Published
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119
llm_pretrain
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Evaluating and Aligning CodeLLMs on Human Preference
Paper • 2412.05210 • Published • 51 -
Evaluating Language Models as Synthetic Data Generators
Paper • 2412.03679 • Published • 49 -
Yi-Lightning Technical Report
Paper • 2412.01253 • Published • 29 -
Large Language Model-Brained GUI Agents: A Survey
Paper • 2411.18279 • Published • 32 -
O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?
Paper • 2411.16489 • Published • 49 -
Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
Paper • 2411.14405 • Published • 62 -
Natural Language Reinforcement Learning
Paper • 2411.14251 • Published • 31 -
The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use
Paper • 2411.10323 • Published • 35 -
Large Language Models Can Self-Improve in Long-context Reasoning
Paper • 2411.08147 • Published • 67 -
A Survey of Small Language Models
Paper • 2410.20011 • Published • 44 -
GPT-4o System Card
Paper • 2410.21276 • Published • 85 -
Qwen2.5-Coder Technical Report
Paper • 2409.12186 • Published • 150 -
DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search
Paper • 2408.08152 • Published • 60 -
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
Paper • 2406.11931 • Published • 65 -
Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
Paper • 2408.06195 • Published • 74 -
Qwen2.5 Technical Report
Paper • 2412.15115 • Published • 372 -
GUI Agents: A Survey
Paper • 2412.13501 • Published • 30 -
DeepSeek-V3 Technical Report
Paper • 2412.19437 • Published • 67 -
Direct Language Model Alignment from Online AI Feedback
Paper • 2402.04792 • Published • 33 -
Solving math word problems with process- and outcome-based feedback
Paper • 2211.14275 • Published • 10 -
The Lessons of Developing Process Reward Models in Mathematical Reasoning
Paper • 2501.07301 • Published • 100 -
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 116 -
OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
Paper • 2501.09751 • Published • 49 -
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
Paper • 2501.09686 • Published • 41 -
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 240 -
s1: Simple test-time scaling
Paper • 2501.19393 • Published • 125 -
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
Paper • 2501.17161 • Published • 123 -
Qwen2.5-1M Technical Report
Paper • 2501.15383 • Published • 72 -
Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 32 -
Pre-training Small Base LMs with Fewer Tokens
Paper • 2404.08634 • Published • 36 -
CodecLM: Aligning Language Models with Tailored Synthetic Data
Paper • 2404.05875 • Published • 18 -
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Paper • 2404.03648 • Published • 29 -
ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline
Paper • 2404.02893 • Published • 23 -
Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
Paper • 2503.24290 • Published • 63 -
START: Self-taught Reasoner with Tools
Paper • 2503.04625 • Published • 114 -
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
Paper • 2503.21460 • Published • 78 -
A Comprehensive Survey on Long Context Language Modeling
Paper • 2503.17407 • Published • 50 -
Gemma 3 Technical Report
Paper • 2503.19786 • Published • 53 -
Open Deep Search: Democratizing Search with Open-source Reasoning Agents
Paper • 2503.20201 • Published • 48 -
A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond
Paper • 2503.21614 • Published • 42 -
Why Do Multi-Agent LLM Systems Fail?
Paper • 2503.13657 • Published • 48 -
Process-based Self-Rewarding Language Models
Paper • 2503.03746 • Published • 40 -
Rethinking Reflection in Pre-Training
Paper • 2504.04022 • Published • 80 -
DeepSeek-R1 Thoughtology: Let's <think> about LLM Reasoning
Paper • 2504.07128 • Published • 86 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 264 -
Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
Paper • 2506.01939 • Published • 173 -
ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
Paper • 2505.24864 • Published • 133 -
The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models
Paper • 2505.22617 • Published • 126 -
Scaling Test-time Compute for LLM Agents
Paper • 2506.12928 • Published • 61 -
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Paper • 2505.24863 • Published • 95 -
Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models
Paper • 2506.06395 • Published • 129