Rethinking the Generation of High-Quality CoT Data from the Perspective
  of LLM-Adaptive Question Difficulty Grading
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.
6809ae47c6faa064f324307d
Qianjin Yu
claimed_verified
2025-04-24T09:10:26.728Z
652f979c61ce8120849bb72f
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Qianjin Yu
user
USTCYu
6809ae47c6faa064f324307e
Keyu Wu
6809ae47c6faa064f324307f
Zihan Chen
6809ae47c6faa064f3243080
Chushu Zhang
admin_assigned
2025-04-24T11:58:35.159Z
66c31c91f1685a3adefef31d
/avatars/efbd2e6f813408b4092621efe8254e35.svg
zhangchushu
user
zcs1234
6809ae47c6faa064f3243081
Manlin Mei
admin_assigned
2025-04-24T11:58:13.997Z
672ad3ee751a2b4a6f7e064d
https://cdn-avatars.huggingface.co/v1/production/uploads/672ad3ee751a2b4a6f7e064d/xSKXBmXIXCSBmMJ1dhols.jpeg
MeiManlin
user
MeiManlin
6809ae47c6faa064f3243082
Lingjun Huang
6809ae47c6faa064f3243083
Fang Tan
6809ae47c6faa064f3243084
Yongsheng Du
6809ae47c6faa064f3243085
Kunlin Liu
admin_assigned
2025-04-24T11:57:32.644Z
64be320fcf4f379eebb8903c
/avatars/3e423729c7081235a51de5f0144eef4b.svg
Liu
user
Kunlinliu2
6809ae47c6faa064f3243086
Yurui Zhu
Rethinking the Generation of High-Quality CoT Data from the Perspective
  of LLM-Adaptive Question Difficulty Grading
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.
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.
chain-of-thought CoT large language models LLMs LLM-Adaptive difficulty levels supervised fine-tuning SFT complex mathematical competitions code generation tasks