Token-Regulated Group Relative Policy Optimization for Stable Reinforcement Learning in Large Language Models

• URL: http://arxiv.org/abs/2511.00066v1
• Date: Wed, 29 Oct 2025 08:07:47 GMT
• Title: Token-Regulated Group Relative Policy Optimization for Stable Reinforcement Learning in Large Language Models
• Authors: Tue Le, Nghi D. Q. Bui, Linh Ngo Van, Trung Le,
• Abstract summary: Group Relative Policy Optimization (GRPO) has demonstrated strong performance.<n>It suffers from a critical issue: low-probability tokens disproportionately dominate gradient updates.<n>This imbalance leads to unstable training and suppresses the contribution of high-probability tokens.
• Score: 18.785063555637613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful approach for strengthening the reasoning capabilities of large language models (LLMs). Among existing algorithms, Group Relative Policy Optimization (GRPO) has demonstrated strong performance, yet it suffers from a critical issue: low-probability tokens disproportionately dominate gradient updates due to their inherently large gradient magnitudes. This imbalance leads to unstable training and suppresses the contribution of high-probability tokens that are more reliable for learning. In this work, we introduce Token-Regulated Group Relative Policy Optimization (TR-GRPO), a simple yet effective extension of GRPO that assigns token-level weights positively correlated with the model's predicted probability. By downweighting low-probability tokens and emphasizing high-probability ones, TR-GRPO mitigates gradient over-amplification while preserving informative learning signals. Extensive experiments demonstrate that TR-GRPO consistently outperforms GRPO across RLVR tasks, including logic, math, and agentic reasoning, highlighting the importance of regulating token contributions during RL training and establishing TR-GRPO as a robust framework for enhancing LLM reasoning.

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