TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908407647174656 |
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| author | Li, Zhong-Zhi Liang, Xiao Tang, Zihao Ji, Lei Wang, Peijie Xu, Haotian W, Xing Huang, Haizhen Deng, Weiwei Gong, Yeyun Guo, Zhijiang Liu, Xiao Yin, Fei Liu, Cheng-Lin |
| author_facet | Li, Zhong-Zhi Liang, Xiao Tang, Zihao Ji, Lei Wang, Peijie Xu, Haotian W, Xing Huang, Haizhen Deng, Weiwei Gong, Yeyun Guo, Zhijiang Liu, Xiao Yin, Fei Liu, Cheng-Lin |
| contents | Large Language Models (LLMs) have recently achieved remarkable progress by leveraging Reinforcement Learning and extended Chain-of-Thought (CoT) techniques. However, the challenge of performing efficient language reasoning--especially during inference with extremely long outputs--has drawn increasing attention from the research community. In this work, we propose a dynamic ratio-based training pipeline that does not rely on sophisticated data annotations or interpolation between multiple models. We continuously balance the weights between the model's System-1 and System-2 data to eliminate redundant reasoning processes while preserving the model's reasoning capability. We validate our approach across models on DeepSeek-R1-Distill-7B and DeepSeek-R1-Distill-14B and on a diverse set of benchmarks with varying difficulty levels. Our method significantly reduces the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning. Our code and data will be available soon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02678 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression Li, Zhong-Zhi Liang, Xiao Tang, Zihao Ji, Lei Wang, Peijie Xu, Haotian W, Xing Huang, Haizhen Deng, Weiwei Gong, Yeyun Guo, Zhijiang Liu, Xiao Yin, Fei Liu, Cheng-Lin Computation and Language Computational Engineering, Finance, and Science Numerical Analysis Large Language Models (LLMs) have recently achieved remarkable progress by leveraging Reinforcement Learning and extended Chain-of-Thought (CoT) techniques. However, the challenge of performing efficient language reasoning--especially during inference with extremely long outputs--has drawn increasing attention from the research community. In this work, we propose a dynamic ratio-based training pipeline that does not rely on sophisticated data annotations or interpolation between multiple models. We continuously balance the weights between the model's System-1 and System-2 data to eliminate redundant reasoning processes while preserving the model's reasoning capability. We validate our approach across models on DeepSeek-R1-Distill-7B and DeepSeek-R1-Distill-14B and on a diverse set of benchmarks with varying difficulty levels. Our method significantly reduces the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning. Our code and data will be available soon. |
| title | TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression |
| topic | Computation and Language Computational Engineering, Finance, and Science Numerical Analysis |
| url | https://arxiv.org/abs/2506.02678 |