title: LLMs Can Easily Learn to Reason from Demonstrations Structure, not
    content, is what matters!
summary: Large reasoning models (LRMs) tackle complex reasoning problems by following
  long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking,
  and self-validation. However, the training techniques and data requirements to
  elicit Long CoT remain poorly understood. In this work, we find that a Large
  Language model (LLM) can effectively learn Long CoT reasoning through
  data-efficient supervised fine-tuning (SFT) and parameter-efficient low-rank
  adaptation (LoRA). With just 17k long CoT training samples, the
  Qwen2.5-32B-Instruct model achieves significant improvements on a wide range of
  math and coding benchmarks, including 56.7% (+40.0%) on AIME 2024 and 57.0%
  (+8.1%) on LiveCodeBench, competitive to the proprietary o1-preview model's
  score of 44.6% and 59.1%. More importantly, we find that the structure of Long
  CoT is critical to the learning process, whereas the content of individual
  reasoning steps has minimal impact. Perturbations affecting content, such as
  training on incorrect samples or removing reasoning keywords, have little
  impact on performance. In contrast, structural modifications that disrupt
  logical consistency in the Long CoT, such as shuffling or deleting reasoning
  steps, significantly degrade accuracy. For example, a model trained on Long CoT
  samples with incorrect answers still achieves only 3.2% lower accuracy compared
  to training with fully correct samples. These insights deepen our understanding
  of how to elicit reasoning capabilities in LLMs and highlight key
  considerations for efficiently training the next generation of reasoning
  models. This is the academic paper of our previous released Sky-T1-32B-Preview
  model. Codes are available at https://github.com/NovaSky-AI/SkyThought.
paper_authors._id: 67ac1c6436464325ebe3c6e3
paper_authors.name: Dacheng Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T09:18:06.131Z
paper_authors._id: 63715b25ffc0489ed7d1f415
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63715b25ffc0489ed7d1f415/xZJepbs0LRqFbW1knnBKR.jpeg
paper_authors.fullname: Dacheng Li
paper_authors.type: user
paper_authors.user: DachengLi
paper_authors._id: 67ac1c6436464325ebe3c6e4
paper_authors.name: Shiyi Cao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-14T08:01:33.967Z
paper_authors._id: 64ebbae6895a36ab28de811a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ebbae6895a36ab28de811a/gBiaQP4paS4L13eu-yRm7.jpeg
paper_authors.fullname: Shiyi Cao
paper_authors.type: user
paper_authors.user: eva98
paper_authors._id: 67ac1c6436464325ebe3c6e5
paper_authors.name: Tyler Griggs
paper_authors._id: 67ac1c6436464325ebe3c6e6
paper_authors.name: Shu Liu
paper_authors._id: 67ac1c6436464325ebe3c6e7
paper_authors.name: Xiangxi Mo
paper_authors._id: 67ac1c6436464325ebe3c6e8
paper_authors.name: Shishir G. Patil
paper_authors._id: 67ac1c6436464325ebe3c6e9
paper_authors.name: Matei Zaharia
paper_authors._id: 67ac1c6436464325ebe3c6ea
paper_authors.name: Joseph E. Gonzalez
paper_authors._id: 67ac1c6436464325ebe3c6eb
paper_authors.name: Ion Stoica
paper_title: LLMs Can Easily Learn to Reason from Demonstrations Structure, not
    content, is what matters!
paper_summary: Large reasoning models (LRMs) tackle complex reasoning problems by following
  long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking,
  and self-validation. However, the training techniques and data requirements to
  elicit Long CoT remain poorly understood. In this work, we find that a Large
  Language model (LLM) can effectively learn Long CoT reasoning through
  data-efficient supervised fine-tuning (SFT) and parameter-efficient low-rank
  adaptation (LoRA). With just 17k long CoT training samples, the
  Qwen2.5-32B-Instruct model achieves significant improvements on a wide range of
  math and coding benchmarks, including 56.7% (+40.0%) on AIME 2024 and 57.0%
  (+8.1%) on LiveCodeBench, competitive to the proprietary o1-preview model's
  score of 44.6% and 59.1%. More importantly, we find that the structure of Long
  CoT is critical to the learning process, whereas the content of individual
  reasoning steps has minimal impact. Perturbations affecting content, such as
  training on incorrect samples or removing reasoning keywords, have little
  impact on performance. In contrast, structural modifications that disrupt
  logical consistency in the Long CoT, such as shuffling or deleting reasoning
  steps, significantly degrade accuracy. For example, a model trained on Long CoT
  samples with incorrect answers still achieves only 3.2% lower accuracy compared
  to training with fully correct samples. These insights deepen our understanding
  of how to elicit reasoning capabilities in LLMs and highlight key
  considerations for efficiently training the next generation of reasoning
  models. This is the academic paper of our previous released Sky-T1-32B-Preview
  model. Codes are available at https://github.com/NovaSky-AI/SkyThought.
paper_ai_summary: Data-efficient supervised fine-tuning and parameter-efficient low-rank adaptation enable large language models to learn long chain-of-thought reasoning effectively, with structural correctness being more crucial than content accuracy in training samples.
paper_ai_keywords: Large reasoning models long chain-of-thoughts Large Language model data-efficient supervised fine-tuning parameter-efficient low-rank adaptation Qwen2.5-32B-Instruct model AIME 2024 LiveCodeBench Sky-T1-32B-Preview model