NeurIPS 2025

Bandit Guided Submodular Curriculum for Adaptive
Subset Selection
Prateek Chanda∗
Prayas Agrawal∗
Saral Sureka
Lokesh Reddy Polu
Atharv Kshirsagar
Ganesh Ramakrishnan
Department of Computer Science and Engineering,
Indian Institute of Technology Bombay
{prateekch, prayas, ssaral
{lokeshreddypolu, atharvksagar, ganesh}@cse.iitb.ac.in
Abstract
Traditional curriculum learning proceeds from easy to hard samples, yet defining
a reliable notion of difficulty remains elusive. Prior work has used submodular
functions to induce difficulty scores in curriculum learning. We reinterpret adaptive
subset selection and formulate it as a multi-armed bandit problem, where each
arm corresponds to a submodular function guiding sample selection. We introduce
ONLINESUBMOD, a novel online greedy policy that optimizes a utility-driven re-
ward and provably achieves no-regret performance under various sampling regimes.
Empirically, ONLINESUBMOD outperforms both traditional curriculum learning
and bi-level optimization approaches across vision and language datasets, show-
ing superior accuracy-efficiency tradeoffs. More broadly, we show that validation-
driven reward metrics offer a principled way to guide the curriculum schedule. Our
code is publicly available at GitHub 2.
1
Introduction
Curriculum Learning (CL), inspired by cognitive development, posits that training machine learning
models by gradually exposing them to data of increasing complexity can significantly enhance
both learning efficiency and generalization performance [4, 59]. The underlying principle is that
mastering simpler concepts first provides a robust foundation for acquiring more complex ones,
leading to improved convergence and a more effective exploration of the hypothesis space [20].
Empirical evidence shows CL improves model training, particularly in areas like code understanding
[34], enhances graph embeddings through complexity-based ordering [57], mitigates catastrophic
forgetting [2, 27, 51, 45], and boosts le...