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arXiv · 2511.00066

Sharpness-Guided Group Relative Policy Optimization via Probability Shaping

Tue Le·Linh Ngo Van·Trung Le
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Abstract

Reinforcement learning with verifiable rewards (RLVR) has become a practical route to improve large language model reasoning, and Group Relative Policy Optimization (GRPO) is a widely used optimizer in this setting. However, RLVR training is typically performed with limited control over generalization. We revisit GRPO through a robustness-based generalization view, where the generalization loss is upper bounded by a combination of the empirical loss and a sharpness surrogate measured by the gradient norm. Building on this perspective, we propose Sharpness-Guided GRPO (GRPO-SG), a simple token-weighted variant of GRPO that downweights tokens likely to cause overly large gradients, reducing sharp updates and stabilizing optimization, thereby improving generalization. Experiments across mathematical reasoning, logic puzzles and tool-augmented question answering show consistent improvements over GRPO, along with smoother gradient-norm trajectories, supporting GRPO-SG as a simple and effective generalization-oriented upgrade to GRPO for RLVR.

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Tue Le, Linh Ngo Van, Trung Le. 2026-05-13. Sharpness-Guided Group Relative Policy Optimization via Probability Shaping. https://arxiv.org/abs/2511.00066

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