Preference Learning
A learning approach in which an AI system learns from preferences between possible outputs or actions to better align its behavior with desired outcomes.
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Related terms
A method for representing and estimating preferences between possible actions or outcomes to guide an AI system's decision-making.
Preference DataData that records preferences between AI outputs or behaviors, typically used to train or optimize models toward preferred responses.
Preference OptimizationThe process of optimizing an AI system to produce outputs that better match preferred responses, behaviors, or outcomes.
Reinforcement LearningRLA machine learning paradigm where an agent learns to make optimal decisions by taking actions in an environment to maximize cumulative rewards.
Human FeedbackInformation, evaluations, corrections, or preferences provided by human users or reviewers about an AI system's outputs or behavior, which can be used to improve model performance, refine prompts, optimize workflows, or guide future decisions.