Alignment with Fill-In-the-Middle for Enhancing Code Generation

• URL: http://arxiv.org/abs/2508.19532v1
• Date: Wed, 27 Aug 2025 03:15:53 GMT
• Title: Alignment with Fill-In-the-Middle for Enhancing Code Generation
• Authors: Houxing Ren, Zimu Lu, Weikang Shi, Haotian Hou, Yunqiao Yang, Ke Wang, Aojun Zhou, Junting Pan, Mingjie Zhan, Hongsheng Li,
• Abstract summary: We propose a novel approach that splits code snippets into smaller, granular blocks, creating more diverse DPO pairs from the same test cases.<n>Our approach demonstrates significant improvements in code generation tasks, as validated by experiments on benchmark datasets such as HumanEval (+), MBPP (+), APPS, LiveCodeBench, and BigCodeBench.
• Score: 56.791415642365415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The code generation capabilities of Large Language Models (LLMs) have advanced applications like tool invocation and problem-solving. However, improving performance in code-related tasks remains challenging due to limited training data that is verifiable with accurate test cases. While Direct Preference Optimization (DPO) has shown promise, existing methods for generating test cases still face limitations. In this paper, we propose a novel approach that splits code snippets into smaller, granular blocks, creating more diverse DPO pairs from the same test cases. Additionally, we introduce the Abstract Syntax Tree (AST) splitting and curriculum training method to enhance the DPO training. Our approach demonstrates significant improvements in code generation tasks, as validated by experiments on benchmark datasets such as HumanEval (+), MBPP (+), APPS, LiveCodeBench, and BigCodeBench. Code and data are available at https://github.com/SenseLLM/StructureCoder.

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