Learning to Align Human Code Preferences

TL;DR

This paper investigates training strategies for aligning large language models with human code preferences, proposing an adaptive method that improves performance across diverse scenarios.

Contribution

It introduces Adaptive Preference Optimization (APO), a novel dynamic training approach that combines theoretical insights and empirical validation for better code preference alignment.

Findings

APO outperforms existing SFT and S&D strategies in various tasks.

Theoretical analysis supports the effectiveness of APO in different preference scenarios.

Extensive experiments validate APO's superior or comparable performance.

Abstract

Large Language Models (LLMs) have demonstrated remarkable potential in automating software development tasks. While recent advances leverage Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) to align models with human preferences, the optimal training strategy remains unclear across diverse code preference scenarios. This paper systematically investigates the roles of SFT and DPO in aligning LLMs with different code preferences. Through both theoretical analysis and empirical observation, we hypothesize that SFT excels in scenarios with objectively verifiable optimal solutions, while applying SFT followed by DPO (S&D) enables models to explore superior solutions in scenarios without objectively verifiable optimal solutions. Based on the analysis and experimental evidence, we propose Adaptive Preference Optimization (APO), a dynamic integration approach that adaptively amplifies preferred responses, suppresses dispreferred ones, and encourages exploration of potentially superior solutions during training. Extensive experiments across six representative code preference tasks validate our theoretical hypotheses and demonstrate that APO consistently matches or surpasses the performance of existing SFT and S&D strategies. Our work provides both theoretical foundations and practical guidance for selecting appropriate training strategies in different code preference alignment scenarios.