CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation

• URL: http://arxiv.org/abs/2505.10594v1
• Date: Thu, 15 May 2025 08:13:45 GMT
• Title: CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation
• Authors: Ningxin Gui, Qianghuai Jia, Feijun Jiang, Yuling Jiao, dechun wang, Jerry Zhijian Yang,
• Abstract summary: CRPE (Code Reasoning Process Enhancer) is a framework for data synthesis and model training.<n>We develop an enhanced COT-Coder that demonstrates marked improvements in code generation tasks.<n>Our COT-Coder-32B-StepDPO, based on Qwen2.5-Coder-32B-Base, exhibits superior performance with a pass@1 accuracy of 35.08, outperforming GPT4O on the benchmark.
• Score: 5.63821063617385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce CRPE (Code Reasoning Process Enhancer), an innovative three-stage framework for data synthesis and model training that advances the development of sophisticated code reasoning capabilities in large language models (LLMs). Building upon existing system-1 models, CRPE addresses the fundamental challenge of enhancing LLMs' analytical and logical processing in code generation tasks. Our framework presents a methodologically rigorous yet implementable approach to cultivating advanced code reasoning abilities in language models. Through the implementation of CRPE, we successfully develop an enhanced COT-Coder that demonstrates marked improvements in code generation tasks. Evaluation results on LiveCodeBench (20240701-20240901) demonstrate that our COT-Coder-7B-StepDPO, derived from Qwen2.5-Coder-7B-Base, with a pass@1 accuracy of 21.88, exceeds all models with similar or even larger sizes. Furthermore, our COT-Coder-32B-StepDPO, based on Qwen2.5-Coder-32B-Base, exhibits superior performance with a pass@1 accuracy of 35.08, outperforming GPT4O on the benchmark. Overall, CRPE represents a comprehensive, open-source method that encompasses the complete pipeline from instruction data acquisition through expert code reasoning data synthesis, culminating in an autonomous reasoning enhancement mechanism.

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