What are Key Factors for Updates in RL for LLM Reasoning?

The article presents a theoretical analysis of Reinforcement Learning from Verifiable Rewards (RLVR) updates, focusing on how off-policy degree and gradient steps influence importance sampling ratios and update dynamics. It introduces Adaptive Clip Policy Optimization (ACPO), which optimizes clipping boundaries based on empirical variance, and shows that ACPO outperforms existing methods like DAPO and CISPO in experiments with 3B and 7B models across various reasoning tasks. This work is significant for practitioners as it provides a more principled approach to RLVR, potentially leading to improved reasoning capabilities in large language models.