Reinforcement Learning from Human Feedback (RLHF)

Reinforcement learning from human feedback, or RLHF, is a training method where human evaluators rank model outputs and a reward model is learned from their preferences. The base model is then fine-tuned to maximize this reward. For practitioners, RLHF helps align Ai Messages with human values, usability, and quality standards, improving default behavior beyond what is achievable with raw pretraining alone. Reinforcement learning from human feedback, abbreviated RLHF, is a training method where humans rate or rank model outputs and a reward model is learned from those preferences. The base model is then fine-tuned to maximize that reward. For practitioners, RLHF helps align Ai Messages with human values, usability standards, and safety guidelines.