title: TL;DR: Too Long, Do Re-weighting for Effcient LLM Reasoning Compression
summary: 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.
paper_authors._id: 68407021a88ca303f9bfd02f
paper_authors.name: Zhong-Zhi Li
paper_authors._id: 68407021a88ca303f9bfd030
paper_authors.name: Xiao Liang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-06-04T16:11:34.763Z
paper_authors._id: 63fb6e281b4b1bd4e7ffc5be
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1677422062937-noauth.jpeg
paper_authors.fullname: Xiao Liu
paper_authors.type: user
paper_authors.user: lx865712528
paper_authors._id: 68407021a88ca303f9bfd031
paper_authors.name: Zihao Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T07:38:53.867Z
paper_authors._id: 67ecf7e9602e1ad720c30b60
paper_authors.avatarUrl: /avatars/fe572d7c94c6dd679f6d49d4ba0e0223.svg
paper_authors.fullname: Zihao Tang
paper_authors.type: user
paper_authors.user: TangZihao
paper_authors._id: 68407021a88ca303f9bfd032
paper_authors.name: Lei Ji
paper_authors._id: 68407021a88ca303f9bfd033
paper_authors.name: Peijie Wang
paper_authors._id: 68407021a88ca303f9bfd034
paper_authors.name: Haotian Xu
paper_authors._id: 68407021a88ca303f9bfd035
paper_authors.name: Xing W
paper_authors._id: 68407021a88ca303f9bfd036
paper_authors.name: Haizhen Huang
paper_authors._id: 68407021a88ca303f9bfd037
paper_authors.name: Weiwei Deng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T07:39:37.179Z
paper_authors._id: 642da49dc1adada5b345c72b
paper_authors.avatarUrl: /avatars/5fa8d345a7248246454444999756105b.svg
paper_authors.fullname: deng
paper_authors.type: user
paper_authors.user: weiweideng
paper_authors._id: 68407021a88ca303f9bfd038
paper_authors.name: Ying Nian Wu
paper_authors._id: 68407021a88ca303f9bfd039
paper_authors.name: Yeyun Gong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T07:39:25.689Z
paper_authors._id: 643f615aa16cd6d1f4c581de
paper_authors.avatarUrl: /avatars/47753a3e82b44f81881600c52e1e8495.svg
paper_authors.fullname: Yeyun Gong
paper_authors.type: user
paper_authors.user: yegong
paper_authors._id: 68407021a88ca303f9bfd03a
paper_authors.name: Zhijiang Guo
paper_authors._id: 68407021a88ca303f9bfd03b
paper_authors.name: Xiao Liu
paper_authors._id: 68407021a88ca303f9bfd03c
paper_authors.name: Fei Yin
paper_authors._id: 68407021a88ca303f9bfd03d
paper_authors.name: Cheng-Lin Liu
paper_title: TL;DR: Too Long, Do Re-weighting for Effcient LLM Reasoning Compression
paper_summary: 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.
paper_ai_summary: A dynamic ratio-based training pipeline reduces output tokens in large language models without sacrificing reasoning accuracy.
paper_ai_keywords: Reinforcement Learning Chain-of-Thought language reasoning dynamic ratio-based training System-1 System-2 DeepSeek-R1-Distill-7B DeepSeek-R1-Distill-14B