Large Language Models (LLMs) often generate unnecessarily verbose Chain-of-Thought (CoT) reasoning that increases computational costs and latency without proportional performance gains. In this paper, we propose Fine-grained Group policy Optimization (FGO), a Reinforcement Learning (RL) algorithm that refines group responses by subdividing them and assigning appropriate weights based on length and entropy, thereby enabling effective CoT compression. Meanwhile, as an enhanced variant of Group Relative Policy Optimization (GRPO), FGO successfully addresses two major limitations of the GRPO: inefficient data utilization and entropy collapse. We evaluate FGO on multiple reasoning LLMs and benchmarks, including MATH500, AIME24, AMC23, and Minerva. Experimental results show that FGO achieves efficient CoT compression without degrading performance, and simultaneously resolves the key limitations of GRPO. Code: https://github.com/Mr-XcHan/FGO.
@article{arxiv.2602.10048,
title = {Long Chain-of-Thought Compression via Fine-Grained Group Policy Optimization},
author = {Xinchen Han and Hossam Afifi and Michel Marot and Xilu Wang and Lu Yin},
journal= {arXiv preprint arXiv:2602.10048},
year = {2026}
}
Comments
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2026