title: Rethinking the Generation of High-Quality CoT Data from the Perspective
    of LLM-Adaptive Question Difficulty Grading
summary: Recently, DeepSeek-R1 (671B) (DeepSeek-AIet al., 2025) has demonstrated its
  excellent reasoning ability in complex tasks and has publiclyshared its
  methodology. This provides potentially high-quality chain-of-thought (CoT) data
  for stimulating the reasoning abilities of small-sized large language models
  (LLMs). To generate high-quality CoT data for different LLMs, we seek an
  efficient method for generating high-quality CoT data with LLM-Adaptive
  questiondifficulty levels. First, we grade the difficulty of the questions
  according to the reasoning ability of the LLMs themselves and construct a
  LLM-Adaptive question database. Second, we sample the problem database based on
  a distribution of difficulty levels of the questions and then use DeepSeek-R1
  (671B) (DeepSeek-AI et al., 2025) to generate the corresponding high-quality
  CoT data with correct answers. Thanks to the construction of CoT data with
  LLM-Adaptive difficulty levels, we have significantly reduced the cost of data
  generation and enhanced the efficiency of model supervised fine-tuning (SFT).
  Finally, we have validated the effectiveness and generalizability of the
  proposed method in the fields of complex mathematical competitions and code
  generation tasks. Notably, with only 2k high-quality mathematical CoT data, our
  ZMath-32B surpasses DeepSeek-Distill-32B in math reasoning task. Similarly,
  with only 2k high-quality code CoT data, our ZCode-32B surpasses
  DeepSeek-Distill-32B in code reasoning tasks.
paper_authors._id: 6809ae47c6faa064f324307d
paper_authors.name: Qianjin Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-24T09:10:26.728Z
paper_authors._id: 652f979c61ce8120849bb72f
paper_authors.avatarUrl: /avatars/bac0c1697ef26ffbb455a8b59c16b783.svg
paper_authors.fullname: Qianjin Yu
paper_authors.type: user
paper_authors.user: USTCYu
paper_authors._id: 6809ae47c6faa064f324307e
paper_authors.name: Keyu Wu
paper_authors._id: 6809ae47c6faa064f324307f
paper_authors.name: Zihan Chen
paper_authors._id: 6809ae47c6faa064f3243080
paper_authors.name: Chushu Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:58:35.159Z
paper_authors._id: 66c31c91f1685a3adefef31d
paper_authors.avatarUrl: /avatars/efbd2e6f813408b4092621efe8254e35.svg
paper_authors.fullname: zhangchushu
paper_authors.type: user
paper_authors.user: zcs1234
paper_authors._id: 6809ae47c6faa064f3243081
paper_authors.name: Manlin Mei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:58:13.997Z
paper_authors._id: 672ad3ee751a2b4a6f7e064d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/672ad3ee751a2b4a6f7e064d/xSKXBmXIXCSBmMJ1dhols.jpeg
paper_authors.fullname: MeiManlin
paper_authors.type: user
paper_authors.user: MeiManlin
paper_authors._id: 6809ae47c6faa064f3243082
paper_authors.name: Lingjun Huang
paper_authors._id: 6809ae47c6faa064f3243083
paper_authors.name: Fang Tan
paper_authors._id: 6809ae47c6faa064f3243084
paper_authors.name: Yongsheng Du
paper_authors._id: 6809ae47c6faa064f3243085
paper_authors.name: Kunlin Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:57:32.644Z
paper_authors._id: 64be320fcf4f379eebb8903c
paper_authors.avatarUrl: /avatars/3e423729c7081235a51de5f0144eef4b.svg
paper_authors.fullname: Liu
paper_authors.type: user
paper_authors.user: Kunlinliu2
paper_authors._id: 6809ae47c6faa064f3243086
paper_authors.name: Yurui Zhu
paper_title: Rethinking the Generation of High-Quality CoT Data from the Perspective
    of LLM-Adaptive Question Difficulty Grading
paper_summary: Recently, DeepSeek-R1 (671B) (DeepSeek-AIet al., 2025) has demonstrated its
  excellent reasoning ability in complex tasks and has publiclyshared its
  methodology. This provides potentially high-quality chain-of-thought (CoT) data
  for stimulating the reasoning abilities of small-sized large language models
  (LLMs). To generate high-quality CoT data for different LLMs, we seek an
  efficient method for generating high-quality CoT data with LLM-Adaptive
  questiondifficulty levels. First, we grade the difficulty of the questions
  according to the reasoning ability of the LLMs themselves and construct a
  LLM-Adaptive question database. Second, we sample the problem database based on
  a distribution of difficulty levels of the questions and then use DeepSeek-R1
  (671B) (DeepSeek-AI et al., 2025) to generate the corresponding high-quality
  CoT data with correct answers. Thanks to the construction of CoT data with
  LLM-Adaptive difficulty levels, we have significantly reduced the cost of data
  generation and enhanced the efficiency of model supervised fine-tuning (SFT).
  Finally, we have validated the effectiveness and generalizability of the
  proposed method in the fields of complex mathematical competitions and code
  generation tasks. Notably, with only 2k high-quality mathematical CoT data, our
  ZMath-32B surpasses DeepSeek-Distill-32B in math reasoning task. Similarly,
  with only 2k high-quality code CoT data, our ZCode-32B surpasses
  DeepSeek-Distill-32B in code reasoning tasks.
paper_ai_summary: The proposed method generates LLM-Adaptive chain-of-thought data for efficient supervised fine-tuning, improving model performance in complex reasoning tasks like mathematics and code generation.
paper_ai_keywords: chain-of-thought CoT large language models LLMs LLM-Adaptive difficulty levels supervised fine-tuning SFT complex mathematical competitions code generation tasks