Article:Wikipedia contributors
Reinforcement Learning from Human Feedback (RLHF): Principles and Applications
Reinforcement learning from human feedback (RLHF) is a machine learning method designed to align intelligent agents with human preferences. Instead of manually specifying hardcoded reward functions, human annotators rank model responses. A reward model is trained on these comparisons to guide policy optimisation using algorithms like Proximal Policy Optimization.
From Video: Building an LLM from Scratch: Neural Networks, Tokenization, and Transformers