DPO
Aligning a model directly on preference pairs, without training a reward model.
Overview
Direct Preference Optimization optimises a language model on pairs of preferred and rejected responses in a single supervised-style step. It solves the complexity of RLHF, which needs a separate reward model and a reinforcement learning loop that is notoriously sensitive to hyperparameters. It exists because the RLHF objective can be rewritten so the optimal policy is available in closed form, which removes the need for the reward model and the sampling loop entirely.
Technology Graph
How DPO connects to the rest of the stack.
Uses and depends on
- RLHF technique Techniques DPO based on RLHF High ✓ source
Applied to
- TRL library LLM Frameworks TRL used for DPO High ✓ source
- Alignment technique Safety & Alignment DPO enables Alignment High ✓ source
Alternatives
Comparisons
Side-by-side breakdowns with the alternatives above.