AI RESEARCH
Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning
arXiv CS.LG
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ArXi:2605.04066v1 Announce Type: cross Reinforcement Learning with Verifiable Rewards (RLVR) is an essential paradigm that enhances the reasoning capabilities of Large Language Models (LLMs). However, existing methods typically rely on static policy optimization schemes that misalign with the model's evolving reasoning capabilities. To address this issue, we propose Adaptive Power-Mean Policy Optimization (APMPO), which comprises two main innovations: Power-Mean Policy Optimization (PMPO) and Feedback-Adaptive Clipping (FAC). Specifically, PMPO