Reinforcement Learning from Human Feedback (RLHF)

Reinforcement learning from human feedback (RLHF) is a training approach used to align AI model behavior with human expectations and preferences. It combines reinforcement learning techniques with structured human evaluations to guide models toward more accurate, helpful, and safe outputs. Instead of relying solely on predefined datasets, this method incorporates human judgments to score responses and refine decision-making policies. Reinforcement learning from human feedback (RLHF) is commonly used in large language model training to improve quality, reduce harmful outputs, and ensure alignment with intended use cases in enterprise and consumer applications.