👉 Reinforcement Learning from Human Feedback (RLHF) is an engineering approach that combines reinforcement learning with human feedback to improve the performance of AI models, particularly in natural language processing tasks. It begins by training a model on human-generated data to understand desired behaviors, then uses human feedback to refine the model's objectives and reward functions. This process involves generating examples of desired and undesired behaviors, which are used to train a reward model. The reward model then guides the reinforcement learning agent to optimize its actions based on these feedback signals, resulting in a more aligned and effective AI system that better meets human expectations and preferences.
