Chengxi Li, Hanlin Wu, Zhicheng Cai, Huan-ang Gao, Wei-Ying Ma, Jingjing Liu, Hao Zhou · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29999 · open-access PDF
Reinforcement learning (RL) based on PPO-style clipping has become an effective approach to improving the reasoning capabilities of large language models (LLMs). However, standard token-level clipping measures policy change only through the conditional probability of the current token, while the prefix conditioning context is itself generated by the policy. This mismatch becomes especially consequential in long-response and off-policy training. We reformulate autoregressive generation as a finite-horizon Markov decision process and derive its performance-difference identity under behavior-poli
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