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Inspired by recent research that recommends starting neural networks trainingwith large learning rates (LRs) to achieve the best generalization, we explorethis hypothesis in detail. Our study clarifies the initial LR ranges thatprovide optimal results for subsequent training with a small LR or weightaveraging. We find that these ranges are in fact significantly narrower thangenerally assumed. We conduct our main experiments in a simplified setup thatallows precise control of the learning rate hyperparameter and validate our keyfindings in a more practical setting.

Large Learning Rates Improve Generalization: But How Large Are We Talking About?