CodeDPO: Aligning Code Models with Self Generated and Verified Source Code

TL;DR

CodeDPO introduces a self-validation framework for code generation models that improves correctness and efficiency by leveraging a novel dataset construction and preference learning approach.

Contribution

It presents a scalable, self-generated dataset creation method that enhances code model training focusing on correctness and efficiency improvements.

Findings

Significant improvements in code correctness and efficiency on five benchmarks.

Effective self-validation mechanism using test case consensus.

Enhanced model performance in real-world code generation scenarios.

Abstract

Code generation models have shown significant potential for programming tasks. However, existing training methods like supervised fine-tuning face key limitations: they do not effectively teach models to prioritize correct over incorrect solutions in ambiguous situations, nor do they effectively optimize the runtime efficiency of the generated code. To address these challenges, we propose CodeDPO, a framework that integrates preference learning into code generation to improve two key code preference factors: code correctness and efficiency. CodeDPO employs a novel dataset construction method, utilizing a self-generation-and-validation mechanism that simultaneously generates and evaluates code and test cases. The underlying assumption is that test cases executable by multiple code snippets provide more reliable validation, and code that passes more tests is more likely to be correct. Through this self-validation process, our PageRank-inspired algorithm iteratively updates the ranking score of each code snippet, ultimately creating a code preference optimization dataset based on correctness and efficiency. CodeDPO is flexible and scalable, generating diverse preference optimization data without depending on external resources. Through comprehensive evaluations of five widely used benchmarks, CodeDPO demonstrates significant improvements in correctness and efficiency compared to existing methods. Our experiments prove that CodeDPO enhances the capabilities of LLMs in code generation and provides a robust foundation for conducting code preference optimization in more complex and challenging real-world scenarios.