Reinforcement Learning from Human Feedback

Reinforcement Learning from Human Feedback (RLHF) is a machine learning paradigm where an agent learns to make decisions based on feedback from human interactions rather than solely from predefined rewards. This approach combines reinforcement learning techniques with human-in-the-loop feedback mechanisms, allowing the model to better align with human values and preferences. RLHF is particularly useful in scenarios where defining explicit reward functions is challenging or impractical. It has been applied in various domains, including natural language processing, robotics, and game playing, where human guidance can significantly enhance the learning process.