Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning 文章

arXiv:2603.13761v2 Announce Type: replace-cross Abstract: Curriculum learning--ordering training examples in a sequence to aid machine learning--takes inspiration from human learning, but has not gained widespread acceptance. Static strategies for scoring item difficulty rely on indirect proxy scores of varying quality and produce curricula that are not specific to the learner at hand. Dynamic approaches base difficulty estimates on gradient information, requiring considerable extra computation during training. We introduce a novel method for measuring the difficulty of individual problem instances that is calibrated to a series of models of increasing competence, and identify \emph{transitional problems} that are consistently easier as model ability increases.