RLHF
Reinforcement learning from human feedback. Human raters compare pairs of model outputs, a reward model learns to predict which one they prefer, and the instruction-tuned model is then optimized against that reward signal. It shapes tone, helpfulness, and refusal behavior after instruction tuning has already taught the model to follow a request.
Why exams ask this
Tested as the step after instruction tuning, not a replacement for it. The exam wants the sequence named: pretrain, then instruction-tune, then align preferences with human feedback, and flags an answer that skips straight from pretraining to RLHF.
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