Conditions for optimality of easy-to-hard curricula

Determine the conditions under which an easy-to-hard curriculum—i.e., training protocols that present examples in increasing order of difficulty—achieves optimal final generalization performance, and characterize alternative difficulty schedules that outperform the easy-to-hard ordering when those conditions are not met.

Background

The paper studies curriculum learning within a teacher–student setting and develops an optimal-control framework to design training schedules. Prior analytical works have largely evaluated fixed curricula and heuristic strategies, leaving open when an easy-to-hard ordering is provably optimal and when other schedules are better. The authors apply their framework to a prototypical binary classification model and find non-monotonic optimal schedules, motivating a formal characterization of the conditions that favor easy-to-hard curricula versus alternatives.

This problem seeks a principled delineation of regimes—across data heterogeneity, model class, and training dynamics—in which an easy-to-hard curriculum is optimal, and identification of schedules that strictly dominate it otherwise.