R1-Compress: Long Chain-of-Thought Compression via Chunk Compression and Search

• URL: http://arxiv.org/abs/2505.16838v1
• Date: Thu, 22 May 2025 16:06:59 GMT
• Title: R1-Compress: Long Chain-of-Thought Compression via Chunk Compression and Search
• Authors: Yibo Wang, Li Shen, Huanjin Yao, Tiansheng Huang, Rui Liu, Naiqiang Tan, Jiaxing Huang, Kai Zhang, Dacheng Tao,
• Abstract summary: Chain-of-Thought (CoT) reasoning enhances large language models (LLMs) by enabling step-by-step problem-solving.<n>CoT's extension to Long-CoT introduces substantial computational overhead due to increased token length.<n>We propose R1-Compress, a two-stage chunk-level compression framework that preserves both local information and coherence.
• Score: 61.4807238517108
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chain-of-Thought (CoT) reasoning enhances large language models (LLMs) by enabling step-by-step problem-solving, yet its extension to Long-CoT introduces substantial computational overhead due to increased token length. Existing compression approaches -- instance-level and token-level -- either sacrifice essential local reasoning signals like reflection or yield incoherent outputs. To address these limitations, we propose R1-Compress, a two-stage chunk-level compression framework that preserves both local information and coherence. Our method segments Long-CoT into manageable chunks, applies LLM-driven inner-chunk compression, and employs an inter-chunk search mechanism to select the short and coherent sequence. Experiments on Qwen2.5-Instruct models across MATH500, AIME24, and GPQA-Diamond demonstrate that R1-Compress significantly reduces token usage while maintaining comparable reasoning accuracy. On MATH500, R1-Compress achieves an accuracy of 92.4%, with only a 0.6% drop compared to the Long-CoT baseline, while reducing token usage by about 20%. Source code will be available at https://github.com/w-yibo/R1-Compress

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