Reinforcement Learning from Human Feedback is a training approach that uses human preferences or ratings to guide a model toward more useful or acceptable outputs.
RLHF is often used to align language-model behavior with user expectations, but it depends on feedback quality, evaluator consistency, safety policy, and ongoing evaluation.