What is Reinforcement Learning from Human Feedback (RLHF)?

Training by reward signals where an agent learns actions that maximize long-term return.

A model that scores outputs based on preference signals, often used in RLHF pipelines.

A training method that fine-tunes models directly on preference pairs without needing a separate reward model.

Training approaches that let a model keep learning from new data without forgetting prior knowledge.

Learning or adapting behavior from only a small number of examples.

Performance degradation over time as real-world conditions diverge from training assumptions.