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arXiv · 2508.04349

GTPO and GRPO-S: Token and Sequence-Level Reward Shaping with Policy Entropy

Hongze Tan·Zihan Wang·Jianfei Pan·Jinghao Lin·Hao Wang·Yifan Wu·Tao Chen·Zhihang Zheng·Zhihao Tang·Haihua Yang
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Abstract

Reinforcement Learning (RL) is pivotal for enhancing Large Language Model (LLM) reasoning, yet mainstream algorithms such as GRPO and DAPO remain constrained by a coarse-grained credit assignment paradigm, where all tokens within the same response receive the identical reward. In this paper, we propose Dynamic Entropy Weighting, systematically define entropy-based weight ratios $\frac{H_{i,t}}{\sum_{k=1}^{n} H_{k,t}}$ and similar variants to redistribute rewards and get fine-grained rewards through two new algorithms: Group Token Policy Optimization (GTPO), which assigns an entropy-weighted reward to each token and synthesizes token-specific advantage function to drive the model toward optimal path, and the analogous algorithm Sequence-Level GRPO (GRPO-S), which extends this design to the sequence level and exhibits superior stability in long Chain-of-Thought (CoT) reasoning tasks.

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Hongze Tan, Zihan Wang, Jianfei Pan, Jinghao Lin, Hao Wang, Yifan Wu, Tao Chen, Zhihang Zheng, Zhihao Tang, Haihua Yang. 2025-08-06. GTPO and GRPO-S: Token and Sequence-Level Reward Shaping with Policy Entropy. https://arxiv.org/abs/2508.04349

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