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Experimental results have shown that curriculum learning, i.e., presentingsimpler examples before more complex ones, can improve the efficiency oflearning. Some recent theoretical results also showed that changing thesampling distribution can help neural networks learn parities, with formalresults only for large learning rates and one-step arguments. Here we show aseparation result in the number of training steps with standard (bounded)learning rates on a common sample distribution: if the data distribution is amixture of sparse and dense inputs, there exists a regime in which a 2-layerReLU neural network trained by a curriculum noisy-GD (or SGD) algorithm thatuses sparse examples first, can learn parities of sufficiently large degree,while any fully connected neural network of possibly larger width or depthtrained by noisy-GD on the unordered samples cannot learn without additionalsteps. We also provide experimental results supporting the qualitativeseparation beyond the specific regime of the theoretical results.

Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs