Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
Authors: name: Zihe Liu, name: Jiashun Liu, name: Yancheng He, name: Weixun Wang, name: Jiaheng Liu, name: Ling Pan, name: Xinyu Hu, name: Shaopan Xiong, name: Ju Huang, name: Jian Hu, name: Shengyi Huang, name: Siran Yang, name: Jiamang Wang, name: Wenbo Su, name: Bo Zheng

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.