What Is Direct Preference Optimization (DPO)? A Simpler Alternative to RLHF

Direct Preference Optimization (DPO) trains AI policies by directly optimizing from pairs of preferred and rejected responses using a classification objective anchored to a reference model, differing from traditional RLHF methods. This approach clarifies the operational boundaries and evaluation criteria distinct from reinforcement learning pipelines that use reward models.