The Crucial Role of Samplers in Online Direct Preference Optimization
Keywords: direct preference optimization, online DPO, tabular softmax policy
TL;DR: We study convergence rates of (online) DPO from optimization perspective, and show the impact of samplers through a theoretical separation and empirical experiments.
Abstract: Direct Preference Optimization (DPO) has emerged as a stable, scalable, and efficient solution for language model alignment.
Despite its empirical success, the optimization properties, particularly the impact of samplers on its convergence rates, remain under-explored. In this paper, we provide a rigorous analysis of DPO's convergence rates with different sampling strategies under the exact gradient setting, revealing a surprising separation: uniform sampling achieves $\textbf{linear}$ convergence, while our proposed online sampler achieves $\textbf{quadratic}$ convergence. We further adapt the sampler to practical settings by incorporating posterior distributions and logit mixing, demonstrating improvements over previous methods. For example, it outperforms vanilla DPO by over $7.4$% on Safe-RLHF dataset. Our results not only offer insights into the theoretical understanding of DPO but also pave the way for further algorithm designs.
Supplementary Material: zip
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 1735
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