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

TL;DR

CRPE is a novel three-stage framework that significantly enhances the reasoning capabilities of large language models for code generation, leading to state-of-the-art performance on benchmark datasets.

Contribution

The paper introduces CRPE, a comprehensive and open-source framework for improving code reasoning in LLMs through data synthesis and training, achieving superior results.

Findings

COT-Coder-7B-StepDPO achieves pass@1 of 21.88, surpassing similar models.

COT-Coder-32B-StepDPO achieves pass@1 of 35.08, outperforming GPT4O.

CRPE improves code generation accuracy and reasoning abilities.

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.