Reinforcement Learning from Human Feedback (RLHF) is a technique that utilizes human feedback to fine-tune language models, making them more aligned with human values and preferences. The process involves three main steps: supervised fine-tuning (SFT), training a reward model based on preference data, and applying reinforcement learning to teach the SFT model the human preference policy through the reward model. OpenAI's ChatGPT is an example of an LLM that has been trained using RLHF. CATEGORIES: 1. Artificial Intelligence 2. Machine Learning 3. Reinforcement Learning