Rethinking the Generation of High-Quality CoT Data from the Perspective of LLM-Adaptive Question Difficulty Grading
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.
Community
The AlM DeepDive Team at Zhongxing Telecom Equipment (ZTE) proposed a high-quality chain-of-Thought (CoT) data
generation method based on LLM-adaptive question difficulty grading, achieving significant improvements in reasoning capabilities across LLMs.
Drawing parallels to Richard Sutton's recent empiricism assertion that "experience is the key to true intelligence," the paper's adaptive difficulty grading can be interpreted as a process of internalizing experience within LLMs.๐
Over look
Recently, the AlM DeepDive Team at Zhongxing Telecom Equipment (ZTE) proposed a high-quality chain-of-Thought (CoT) data generation method based on LLM-adaptive question difficulty grading, achieving significant improvements in reasoning capabilities across LLMs.
Drawing parallels to Richard Sutton's recent empiricism assertion that "experience is the key to true intelligence," the paper's adaptive difficulty grading can be interpreted as a process of internalizing experience within LLMs.๐
The related data and model is here:
32B_LLM_AdaptiveMath_data
LLM-Adaptive-CoT-Code-data
LLM-Adaptive-ZMath-model-32B
[๐ค LLM-Adaptive-ZMath-model-32B]
LLM-Adaptive-ZCode-model-32B
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