Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that utilizes human feedback to enhance the performance of models. In this approach, human evaluators assess the outputs generated by the model, providing valuable feedback that informs the learning process. This feedback helps align the model’s behavior with human values and preferences, making it particularly effective in scenarios where defining a clear reward function is challenging. RLHF is essential in applications where human judgment is critical for evaluating the quality of the model’s performance, enabling more intuitive and context-aware AI systems.
