Preference Tuning

A training technique where a model is fine-tuned based on human preferences between pairs of outputs, teaching it to produce responses that people find more helpful, accurate, or appropriate. Preference tuning is a key step in making raw language models behave like useful assistants, and is closely related to reinforcement learning from human feedback.