GMTS: Gradient Magnitude-based Token Selection Improves RLVR Training for LLM Reasoning

• URL: http://arxiv.org/abs/2608.30632v1
• Date: Mon, 31 Aug 2026 11:41:11 GMT
• Title: GMTS: Gradient Magnitude-based Token Selection Improves RLVR Training for LLM Reasoning
• Abstract summary: We introduce the Gradient Magnitude-based Token Selection (GMTS) method to quantify token importance.<n>We find that training on the top 20% tokens ranked by GMTS consistently outperforms entropy-based token selection across three reasoning domains and various model sizes.
• Score: 1.238039401707697
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL), particularly RL with Verifiable Rewards (RLVR), has recently emerged as a central paradigm for enhancing large language models' (LLMs) reasoning abilities, demonstrating remarkable effectiveness across reasoning tasks. Recent studies suggest that high-entropy tokens play an exceptionally important role in model training, since training with only the highest 20% entropy tokens yields significant performance gains. However, why such high-entropy tokens are beneficial remains insufficiently understood. In this work, we find that although high-entropy tokens within one answer tend to correlate with large gradient magnitude, entropy alone fails to consistently reflect token importance across different answers, considering the variations in the answer-level reward signals. Based on this observation, we introduce the Gradient Magnitude-based Token Selection (GMTS) method to quantify token importance, which leverages the entropy-gradient connection to approximate gradient-magnitude rankings for token selection. We find that training on the top 20% tokens ranked by GMTS consistently outperforms entropy-based token selection across three reasoning domains and various model sizes, suggesting that GMTS provides a more fine-grained estimate of token contribution for RLVR training.

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