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This paper theoretically analyzes how curriculum learning, by decomposing complex problems into simpler sub-problems and composing solutions, can dramatically reduce the sample complexity of learning to simulate sequential computations (semiautomata) compared to direct methods, achieving subpolynomial supervision requirements in supervised fine-tuning and exponentially weaker coverage conditions in reinforcement learning with verifiable rewards.