Direct Preference Optimization (DPO)
An alignment algorithm that optimizes policy networks directly using pairwise preference data without reward model training.
DPO simplifies model alignment by bypassing the need for a separate reward model or reinforcement learning loop. It uses a closed-form loss function to optimize policy weights directly against human preferences.
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