Reinforcement Learning from Human Feedback (RLHF) is a machine learning approach where artificial intelligence systems are trained using feedback provided by humans. Instead of relying solely on pre-programmed rules or automated data, RLHF involves people evaluating the AI’s outputs and giving positive or negative feedback. This guidance helps the AI learn which behaviors are desirable and which are not, resulting in more accurate, helpful, and aligned responses. RLHF is commonly used to improve large language models, making them safer and more responsive to human values and preferences.