Reinforcement Learning from Human Feedback (RLHF) is a technique that combines reinforcement learning with human feedback to train language models. It involves using human preferences to guide the training process, with various challenges, including data collection, reward optimization, and preference aggregation. RLHF has potential applications in language model fine-tuning, decision-making, and dialogue system development.
