RLHF
also: reinforcement learning from human feedback
Training a model against a learned reward of human preferences — how raw LLMs become helpful assistants.
RLHF takes a pretrained model and aligns it with what humans actually prefer: collect comparisons of outputs, train a reward model on those preferences, then optimize the LLM against that reward with reinforcement learning (classically PPO). It is the step that turned autocomplete engines into assistants. Simpler successors like DPO reach similar results without the RL machinery.