A Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications
Abstract
With the rapid advancement of large language models (LLMs), aligning policy models with human preferences has become increasingly critical. Direct Preference Optimization (DPO) has emerged as a promising approach for alignment, acting as an RL-free alternative to Reinforcement Learning from Human Feedback (RLHF). Despite DPO's various advancements and inherent limitations, an in-depth review of these aspects is currently lacking in the literature. In this work, we present a review of the challenges and opportunities in DPO, covering theoretical analyses, variants, relevant preference datasets, and applications. Specifically, we categorize recent studies on DPO based on key research questions to provide a thorough understanding of DPO's current landscape. Additionally, we propose several future research directions to offer insights on model alignment for the research community.
Fields of Study & Subjects
Multi-Criteria Decision Making
Publication details
- Journal / source
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- PP
- Issue
- 10
- Pages
- 1–20
- Publication date
- 1 January 2026
- Language
- EN