Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
Reinforcement learning for LLM reasoning has rapidly emerged as a prominent
research area, marked by a significant surge in related studies on both
algorithmic innovations and practical applications. Despite this progress,
several critical challenges remain, including the absence of standardized
guidelines for employing RL techniques and a fragmented understanding of their
underlying mechanisms. Additionally, inconsistent experimental settings,
variations in training data, and differences in model initialization have led
to conflicting conclusions, obscuring the key characteristics of these
techniques and creating confusion among practitioners when selecting
appropriate techniques. This paper systematically reviews widely adopted RL
techniques through rigorous reproductions and isolated evaluations within a
unified open-source framework. We analyze the internal mechanisms, applicable
scenarios, and core principles of each technique through fine-grained
experiments, including datasets of varying difficulty, model sizes, and
architectures. Based on these insights, we present clear guidelines for
selecting RL techniques tailored to specific setups, and provide a reliable
roadmap for practitioners navigating the RL for the LLM domain. Finally, we
reveal that a minimalist combination of two techniques can unlock the learning
capability of critic-free policies using vanilla PPO loss. The results
demonstrate that our simple combination consistently improves performance,
surpassing strategies like GRPO and DAPO.
689aefacfab6fdd2e52ac5ef
Zihe Liu
689aefacfab6fdd2e52ac5f0
Jiashun Liu
689aefacfab6fdd2e52ac5f1
Yancheng He
689aefacfab6fdd2e52ac5f2
Weixun Wang
689aefacfab6fdd2e52ac5f3
Jiaheng Liu
689aefacfab6fdd2e52ac5f4
Ling Pan
689aefacfab6fdd2e52ac5f5
Xinyu Hu
689aefacfab6fdd2e52ac5f6
Shaopan Xiong
689aefacfab6fdd2e52ac5f7
Ju Huang
689aefacfab6fdd2e52ac5f8
Jian Hu
689aefacfab6fdd2e52ac5f9
Shengyi Huang
689aefacfab6fdd2e52ac5fa
Siran Yang
689aefacfab6fdd2e52ac5fb
Jiamang Wang
689aefacfab6fdd2e52ac5fc
Wenbo Su
689aefacfab6fdd2e52ac5fd
Bo Zheng
Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
Reinforcement learning for LLM reasoning has rapidly emerged as a prominent
research area, marked by a significant surge in related studies on both
algorithmic innovations and practical applications. Despite this progress,
several critical challenges remain, including the absence of standardized
guidelines for employing RL techniques and a fragmented understanding of their
underlying mechanisms. Additionally, inconsistent experimental settings,
variations in training data, and differences in model initialization have led
to conflicting conclusions, obscuring the key characteristics of these
techniques and creating confusion among practitioners when selecting
appropriate techniques. This paper systematically reviews widely adopted RL
techniques through rigorous reproductions and isolated evaluations within a
unified open-source framework. We analyze the internal mechanisms, applicable
scenarios, and core principles of each technique through fine-grained
experiments, including datasets of varying difficulty, model sizes, and
architectures. Based on these insights, we present clear guidelines for
selecting RL techniques tailored to specific setups, and provide a reliable
roadmap for practitioners navigating the RL for the LLM domain. Finally, we
reveal that a minimalist combination of two techniques can unlock the learning
capability of critic-free policies using vanilla PPO loss. The results
demonstrate that our simple combination consistently improves performance,
surpassing strategies like GRPO and DAPO.
A systematic review of reinforcement learning techniques for large language model reasoning reveals clear guidelines and demonstrates that a minimalist combination of techniques can improve performance over existing strategies.
reinforcement learning LLM reasoning RL techniques standardized guidelines experimental settings training data model initialization internal mechanisms applicable scenarios core principles datasets model sizes architectures critic-free policies vanilla PPO loss GRPO DAPO