DPO (Direct Preference Optimization)
A simpler alternative to RLHF that skips the reward model.
/ quick answer
DPO fine-tunes directly on preference pairs (chosen vs rejected) with a contrastive loss. Cheaper and more stable than PPO; the default for open-source alignment. A simpler alternative to RLHF that skips the reward model.
/ frequently asked
What is DPO (Direct Preference Optimization)?
DPO fine-tunes directly on preference pairs (chosen vs rejected) with a contrastive loss. Cheaper and more stable than PPO; the default for open-source alignment.
What is an example of DPO (Direct Preference Optimization)?
Most fine-tunes of Llama-3 for chat use DPO on 10k-100k preference pairs.
Why does DPO (Direct Preference Optimization) matter?
A simpler alternative to RLHF that skips the reward model.
What should I learn after DPO (Direct Preference Optimization)?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
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Related concepts
The vocabulary this page depends on.
- →Fine-Tuning
Continuing to train a base model on your own examples to specialize its behavior.
- →RLHF
Reinforcement learning from human feedback, the alignment technique behind ChatGPT.
- →Model Distillation
Training a small model to mimic a large one.
- →LoRA
Low-Rank Adaptation: cheap fine-tuning that patches only small matrices.
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Related tool stacks
The tools that run it in production.
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