AI Glossary Term

What is DPO (Direct Preference Optimization)?

Definition

A training method that fine-tunes models directly on preference pairs without needing a separate reward model.

Related terms

Reinforcement Learning from Human Feedback (RLHF)
A training method that uses human preference signals to shape model behavior.
Post-training
Training steps applied after pretraining, such as instruction tuning, preference optimization, and safety tuning.
Continual Learning
Training approaches that let a model keep learning from new data without forgetting prior knowledge.
Fine-Tuning
Continuing training on domain-specific data to adapt a pre-trained model to a specific task.
Gradient Descent
An optimization method that updates parameters in the direction that reduces error.
Instruction Tuning
Fine-tuning a model on instruction-response pairs to improve task following.

Learn more in our free guides

AI Inference OptimizationBidirectional Recurrent NetworksSecond-Order Optimization and Newton MethodsOdds Ratio Preference Optimization
Browse the full AI GlossaryExplore all free AI guides