TL;DR: Too Long, Do Re-weighting for Effcient LLM Reasoning Compression
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.
68407021a88ca303f9bfd02f
Zhong-Zhi Li
claimed_verified
2025-06-16T12:56:30.638Z
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Zhongzhi Li
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lizhongzhi2022
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Xiao Liang
extracted_confirmed
2025-06-04T16:11:34.763Z
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Xiao Liu
user
lx865712528
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Zihao Tang
admin_assigned
2025-06-05T07:38:53.867Z
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Zihao Tang
user
TangZihao
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Lei Ji
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Peijie Wang
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2025-06-17T13:07:10.549Z
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PeijieWang
user
PeijieWang
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Haotian Xu
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Xing W
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Haizhen Huang
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Weiwei Deng
admin_assigned
2025-06-05T07:39:37.179Z
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deng
user
weiweideng
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Ying Nian Wu
68407021a88ca303f9bfd039
Yeyun Gong
admin_assigned
2025-06-05T07:39:25.689Z
643f615aa16cd6d1f4c581de
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Yeyun Gong
user
yegong
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Zhijiang Guo
68407021a88ca303f9bfd03b
Xiao Liu
claimed_verified
2025-06-23T08:31:47.928Z
6560763e152b659e623865ae
https://cdn-avatars.huggingface.co/v1/production/uploads/6560763e152b659e623865ae/cTT2jGnPU_8XMrUTvqZ2h.jpeg
Xiao Liang
user
MasterVito
68407021a88ca303f9bfd03c
Fei Yin
68407021a88ca303f9bfd03d
Cheng-Lin Liu
TL;DR: Too Long, Do Re-weighting for Effcient LLM Reasoning Compression
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.
A dynamic ratio-based training pipeline reduces output tokens in large language models without sacrificing reasoning accuracy.
Reinforcement Learning Chain-of-Thought language reasoning dynamic ratio-based training System-1 System-2 DeepSeek-R1-Distill-7B DeepSeek-R1-Distill-14B