M-GRPO: Stabilizing Self-Supervised Reinforcement Learning for Large Language Models with Momentum-Anchored Policy Optimization

• URL: http://arxiv.org/abs/2512.13070v1
• Date: Mon, 15 Dec 2025 08:07:23 GMT
• Title: M-GRPO: Stabilizing Self-Supervised Reinforcement Learning for Large Language Models with Momentum-Anchored Policy Optimization
• Authors: Bizhe Bai, Hongming Wu, Peng Ye, Tao Chen,
• Abstract summary: Self-supervised reinforcement learning (RL) presents a promising approach for enhancing the reasoning capabilities of Large Language Models (LLMs)<n>We find that existing methods suffer from a critical failure mode under long-horizon training: a "policy collapse" where performance precipitously degrades.<n>We introduce M-GRPO, a framework that leverages a slowly evolving momentum model to provide a stable training target.<n>We also propose an adaptive filtering method based on the interquartile range (IQR) that dynamically prunes low-entropy trajectories.
• Score: 9.358876832727239
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised reinforcement learning (RL) presents a promising approach for enhancing the reasoning capabilities of Large Language Models (LLMs) without reliance on expensive human-annotated data. However, we find that existing methods suffer from a critical failure mode under long-horizon training: a "policy collapse" where performance precipitously degrades. We diagnose this instability and demonstrate that simply scaling the number of rollouts -- a common strategy to improve performance -- only delays, but does not prevent, this collapse. To counteract this instability, we first introduce M-GRPO (Momentum-Anchored Group Relative Policy Optimization), a framework that leverages a slowly evolving momentum model to provide a stable training target. In addition, we identify that this process is often accompanied by a rapid collapse in policy entropy, resulting in a prematurely confident and suboptimal policy. To specifically address this issue, we propose a second contribution: an adaptive filtering method based on the interquartile range (IQR) that dynamically prunes low-entropy trajectories, preserving essential policy diversity. Our extensive experiments on multiple reasoning benchmarks demonstrate that M-GRPO stabilizes the training process while the IQR filter prevents premature convergence. The combination of these two innovations leads to superior training stability and state-of-the-art performance.

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