Curriculum Learning

Curriculum learning studies whether the order and difficulty of training can help models learn more efficiently or generalize better. Common approaches include easy-to-hard schedules, self-paced sampling based on loss or confidence, adaptive teacher-student selection, and staged curricula for tasks, model representations, or reinforcement-learning environments. It is used in settings from language-model reasoning and preference tuning to robotics, vision, and scientific prediction, with evaluations on reasoning benchmarks, image datasets, and simulated or physical control tasks. Results are mixed: curricula can improve learning speed and data efficiency, but benefits depend on how difficulty is measured, how pacing matches the learner, and whether comparisons control for data exposure and training budget.