RLHF trains a preference or reward model from comparisons, then optimizes the policy while constraining drift from a reference model, often with a KL term. Reward misspecification, overoptimization, and preference bias require held-out human evaluation.
Use equations or tensor shapes where they clarify the claim, then name an experiment or ablation that would distinguish competing explanations.
Get asked follow-ups live, then receive a scored report — like a real MLE interview loop.
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