Rethinking the Generation of High-Quality CoT Data from the Perspective
  of LLM-Adaptive Question Difficulty Grading
Authors: name: Qianjin Yu; name: Keyu Wu; name: Zihan Chen; name: Chushu Zhang; name: Manlin Mei; name: Lingjun Huang; name: Fang Tan; name: Yongsheng Du; name: Kunlin Liu; name: Yurui Zhu
Published: 2025-04-16 09:55:34

Abstract: 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.