Definition

RLHF

Reinforcement Learning from Human Feedback is the post-training process that turned raw text predictors into assistants that follow instructions. It runs in three stages: fine-tune on human-written ideal responses, train a reward model from human rankings of candidate outputs, then optimize the language model with PPO to score higher on that reward model. It teaches presentation, not new facts, and it made ChatGPT possible.