Reinforcement learning from human feedback
RLHF uses human preference evidence to train a reward signal and optimize model behavior. The InstructGPT pipeline combines supervised demonstrations, preference comparisons, reward modeling, and policy optimization.
Overview. RLHF uses human preference evidence to train a reward signal and optimize model behavior.
Context. The InstructGPT pipeline combines supervised demonstrations, preference comparisons, reward modeling, and policy optimization.
Claims and evidence
Claim ledger, 2RLHF uses human preference evidence to train a reward signal and optimize model behavior.
The InstructGPT pipeline combines supervised demonstrations, preference comparisons, reward modeling, and policy optimization.