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arXiv AI By John J. Vastola, Ann Huang, Satpreet H. Singh, Samuel J. Gershman, Kanaka Rajan

Effectiveness of Curriculum Learning Depends on Reward Sparsity and Competing Optima

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The paper investigates how curriculum learning affects reinforcement learning in a minimal policy learning model. It shows that when rewards are dense, curricula offer only marginal benefits, but when rewards are sparse, curricula become essential for learning to occur. The study also highlights that competing objectives—minimizing effort versus maximizing reward—create distinct optima that curricula can help navigate by reducing reward sparsity and reshaping the reward landscape.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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