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A Solvable Model of Adaptive Learning Rate Rescaling: Acceleration, Stability & Scaling
A recurring design principle in modern optimizers is to decouple update magnitude from the raw gradient norm, yet its consequences for learning-curve and resource scaling remain unclear. We isolate this mechanism by studying normalized SGD in a random-feature model with power-law teacher and data co
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