title: Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with
    Difficulty-Aware Prompting
summary: Existing chain-of-thought (CoT) distillation methods can effectively transfer
  reasoning abilities to base models but suffer from two major limitations:
  excessive verbosity of reasoning traces and inadequate adaptability to problem
  difficulty. Long reasoning traces significantly increase inference costs, and
  uniform-length solutions prevent base models from learning adaptive reasoning
  strategies. To address these issues, we propose a difficulty-aware prompting
  (DAP) method to dynamically shorten reasoning traces without performance loss.
  In our approach, a large teacher model first judges each problem's difficulty
  and then rewrites its reasoning traces to an appropriate shorter length,
  yielding concise yet complete reasoning traces. Leveraging the DAP pipeline, we
  curate a distilled dataset called LiteCoT consisting of 100K concise reasoning
  examples, with solutions averaging only 720 tokens (an order of magnitude
  shorter than typical CoTs). Using LiteCoT, we distilled a new family of
  reasoning models called Liter (1.5B, 7B, and 32B) based on the Qwen2.5
  architecture. Experiments show that a student model fine-tuned on just 100K of
  these difficulty-pruned CoT samples outperforms a model distilled on 800K
  original Long CoT samples, while significantly reducing training and inference
  costs. Our method also generalizes well: across 11 diverse benchmarks, the
  shorter difficulty-aware CoTs achieve equal or better accuracy than Long
  chains, using far fewer tokens. For example, on the challenging AIME24 exam,
  our approach reaches 74.2% Pass@1 using only about 5K inference tokens,
  surpassing other methods that consume many more tokens. Our code and data are
  available at https://github.com/Evanwu1125/LiteCoT.
paper_authors._id: 6839ac8c17471fea48833b1c
paper_authors.name: Yifan Wu
paper_authors._id: 6839ac8c17471fea48833b1d
paper_authors.name: Jingze Shi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:45:24.859Z
paper_authors._id: 673ab3647afcea17eb4378fd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/673ab3647afcea17eb4378fd/YQB6zSH1LPxBMUYayIURi.png
paper_authors.fullname: Loser Cheems
paper_authors.type: user
paper_authors.user: JingzeShi
paper_authors._id: 6839ac8c17471fea48833b1e
paper_authors.name: Bingheng Wu
paper_authors._id: 6839ac8c17471fea48833b1f
paper_authors.name: Jiayi Zhang
paper_authors._id: 6839ac8c17471fea48833b20
paper_authors.name: Xiaotian Lin
paper_authors._id: 6839ac8c17471fea48833b21
paper_authors.name: Nan Tang
paper_authors._id: 6839ac8c17471fea48833b22
paper_authors.name: Yuyu Luo
paper_title: Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with
    Difficulty-Aware Prompting
paper_summary: Existing chain-of-thought (CoT) distillation methods can effectively transfer
  reasoning abilities to base models but suffer from two major limitations:
  excessive verbosity of reasoning traces and inadequate adaptability to problem
  difficulty. Long reasoning traces significantly increase inference costs, and
  uniform-length solutions prevent base models from learning adaptive reasoning
  strategies. To address these issues, we propose a difficulty-aware prompting
  (DAP) method to dynamically shorten reasoning traces without performance loss.
  In our approach, a large teacher model first judges each problem's difficulty
  and then rewrites its reasoning traces to an appropriate shorter length,
  yielding concise yet complete reasoning traces. Leveraging the DAP pipeline, we
  curate a distilled dataset called LiteCoT consisting of 100K concise reasoning
  examples, with solutions averaging only 720 tokens (an order of magnitude
  shorter than typical CoTs). Using LiteCoT, we distilled a new family of
  reasoning models called Liter (1.5B, 7B, and 32B) based on the Qwen2.5
  architecture. Experiments show that a student model fine-tuned on just 100K of
  these difficulty-pruned CoT samples outperforms a model distilled on 800K
  original Long CoT samples, while significantly reducing training and inference
  costs. Our method also generalizes well: across 11 diverse benchmarks, the
  shorter difficulty-aware CoTs achieve equal or better accuracy than Long
  chains, using far fewer tokens. For example, on the challenging AIME24 exam,
  our approach reaches 74.2% Pass@1 using only about 5K inference tokens,
  surpassing other methods that consume many more tokens. Our code and data are
  available at https://github.com/Evanwu1125/LiteCoT.
paper_ai_summary: The difficulty-aware prompting method shortens reasoning traces in a dataset, improving model performance and efficiency across various benchmarks.
paper_ai_keywords: chain-of-thought distillation reasoning traces inference costs adaptive reasoning strategies difficulty-aware prompting teacher model distilled dataset LiteCoT reasoning models Liter Qwen2.5 architecture fine-tuned student model training costs accuracy AIME24 exam Pass@1