Pairwise Preference Learning

Pairwise preference learning studies how to infer what people value from choices between alternatives, rather than requiring absolute scores. Common approaches include Bradley–Terry reward models, direct preference optimization for language models, and active learning or dueling-bandit methods that select informative comparisons. The methods support applications from ranking and recommendations to AI alignment, robotics, and policy learning, including settings with noisy labels or limited annotation budgets. A central challenge is that preferences may be heterogeneous, context-dependent, or cyclic, so a single consistent utility score can miss important differences between people and situations.