Reinforcement Learning from Human Feedback

Reinforcement learning from human feedback uses human preferences or ratings to train a model toward outputs that people judge as more useful or appropriate. It is commonly used in systems that generate or retrieve language, code, images, or other content and often works alongside prompts, tools, embeddings, and external knowledge.