AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization

TL;DR

AlphaDPO introduces an adaptive reward margin mechanism for preference optimization, improving alignment and diversity in large language models by balancing policy and reference models dynamically.

Contribution

It proposes $oldsymbol{ extalpha}$-DPO, a novel adaptive preference optimization algorithm with theoretical guarantees, outperforming existing methods like DPO and SimPO in LLM fine-tuning.

Findings

Consistently outperforms DPO and SimPO in empirical evaluations.

Achieves higher win rates in alignment tasks.

Provides theoretical guarantees for adaptive reward margin effectiveness.

Abstract

Aligning large language models (LLMs) with human values and intentions is crucial for their utility, honesty, and safety. Reinforcement learning from human feedback (RLHF) is a popular approach to achieve this alignment, but it faces challenges in computational efficiency and training stability. Recent methods like Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO) have proposed offline alternatives to RLHF, simplifying the process by reparameterizing the reward function. However, DPO depends on a potentially suboptimal reference model, and SimPO's assumption of a fixed target reward margin may lead to suboptimal decisions in diverse data settings. In this work, we propose $\alpha$-DPO, an adaptive preference optimization algorithm designed to address these limitations by introducing a dynamic reward margin. Specifically, $\alpha$-DPO employs an adaptive preference distribution, balancing the policy model and the reference model to achieve personalized reward margins. We provide theoretical guarantees for $\alpha$-DPO, demonstrating its effectiveness as a surrogate optimization objective and its ability to balance alignment and diversity through KL divergence control. Empirical evaluations on AlpacaEval 2 and Arena-Hard show that $\alpha$-DPO consistently outperforms DPO and SimPO across various model settings, establishing it as a robust approach for fine-tuning LLMs. Our method achieves significant improvements in win rates, highlighting its potential as a powerful tool for LLM alignment. The code is available at https://github.com/junkangwu/alpha-DPO