Reward-based fine-tuning updates a pretrained model according to feedback expressed as a numerical reward. The reward may come from people, automated checks, a learned reward model or task-specific measurements, allowing training to favor outputs that better meet an objective.
The method can improve generation or decision-making, but the reward must represent the real goal accurately. A model may exploit shortcuts in an incomplete metric, and optimization can reduce other useful behavior. Careful evaluation, regularization and diverse feedback help manage those risks.
Acronyms and aliases
reward-guided fine-tuning synonym
Related terms
Frequently asked questions
How is reward-based fine-tuning different from supervised fine-tuning?
Supervised fine-tuning learns from target examples. Reward-based fine-tuning receives a score for generated behavior and updates the model to increase expected reward.
Can diffusion models use reward-based fine-tuning?
Yes. A reward can score properties of generated images, video or other samples, and optimization can adapt the diffusion model toward higher-scoring outputs.
