Investigating Sample-Level Sharpness as a Curriculum Scoring Function

Curriculum learning presents training examples to a model in order of increas- ing difficulty, with sample-level difficulty estimations typically relying on metrics that are implicitly deduced from the model’s loss landscape. Yet, the connection to minima sharpness, a key characteristic of the local loss landscape, which is often investigated in the context of generalisation on a model or dataset level, has barely been made in the literature. In this work, we introduce per-sample sharpness as a difficulty estimation that measures how quickly the loss on a single example rises under random, filter-normalized weight perturbations of a trained model, along with a Gradient-Guided variant that perturbs along the example’s own loss gra- dient. We evaluate both on CIFAR-10, CIFAR-100, and the Minimum Viewing Time dataset, comparing sharpness against five established scoring functions and human difficulty labels.We find that (i) sharper minima on a sample-level min- ima correlate with higher difficulty according to other difficulty metrics, imply- ing a related but distinct notion of difficulty, which, however, does not correlate with human perception, (ii) random effects w. r. t. to underlying filter-normalized directions can be mitigated through ensembles, which also increase correlation with gradient-guided sharpness, (iii) sharpness-based curriculum learning outper- forms a random-order baseline but not standard training (iv) under post-training weight quantization, model accuracy collapses at the same perturbation magnitude at which the Sharpness Score ordering itself becomes unreliable, consistent with a transition from a locally convex to a chaotic region of the loss landscape. We share our code on https://anonymous.4open.science/r/curriculum-learning-7302/