Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning

AbstractIterative preference learning, though yielding superior performances, requires online annotated preference labels. In this work, we study strategies to save annotation budgets while achieving competitive or even better performances for iterative preference learning. Built on intuitions from active learning, we empirically show that annotating those response pairs with small margins is generally better than large or random. Besides, experiments under the multi-iteration scenario suggest allocating more annotation budgets in the earlier iterations rather than later ones.