Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning
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 introduces a generalized power-mean objective. This enables the model to adaptively transition from the signal-amplifying behavior of the arithmetic mean to the consistency-enforcing be...
| Authors | Yiming Huang, Zhenbo Shi, Shuzheng Gao et al. |
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| Primary category | cs.CL |
| Published | 11 April 2026 |
| Last updated at source | 7 May 2026 |
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