Beyond the Mean: Multi-Moment Policy Optimization for LLM Reasoning
Reinforcement learning has become a central paradigm for improving the reasoning capabilities of large language models. Existing methods generally aim to reduce the failure probabilities induced across problems. In this paper, we introduce a moment-based perspective on policy optimization for LLM reasoning by treating the failure probability of a randomly sampled problem as a random variable and characterizing optimization objectives through its moments. Under this perspective, many existing methods optimize only a single moment of the failure-probability distribution, leaving its broader distributional structure largely uncharacterized. We propose \textbf{M}ulti-\textbf{M}oment \textbf{P...
| Authors | Yijun Zhang, Yule Xie, Jiaxin Ding et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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