How does Reinforcement Learning from Human Feedback (RLHF) work in LLMs?

RLHF aligns LLM behavior with human preferences by combining supervised fine-tuning, reward modeling, and reinforcement learning optimization.

RLHF is a multi-stage alignment process designed to make LLM outputs more useful, safe, and aligned with human expectations.

The process generally includes:

The model is first trained on massive text corpora using next-token prediction.

Human annotators create high-quality prompt-response examples. The model learns desirable conversational behavior.

Humans rank multiple outputs for the same prompt. A separate reward model learns to predict which outputs humans prefer.

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