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Provable Benefit of SignGD: A Minimal Model Under Heavy-Tailed Class Imbalance
Adaptive and non-Euclidean optimizers often outperform Euclidean methods such as stochastic gradient descent (SGD) in language modeling by a large margin. Existing theory usually explains this gap by assuming favorable smoothness geometry or noise structure tailored to the specific optimizer. We ins
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