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  5. Human Preference Data
Data & Datasetsaka preference pairsaka comparison data

Human Preference Data

Paired comparisons where annotators mark which of two model outputs is better, used to train reward models and DPO.

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Unlike a single gold answer, preference data captures relative quality: helpfulness, honesty, style, or safety. Collection is expensive and noisy, so labs mix expert raters, crowd workers, and model-generated comparisons. Garbage preferences produce sycophantic or overly refused models even if the base checkpoint was strong.

Related terms

Preference DataReinforcement Learning from Human FeedbackDirect Preference OptimizationReward ModelMultimodal DatasetTokenizer Vocabulary