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Correction capability varies dramatically across parameter groups, with normalization-affine parameters offering the most efficient path to improved quantization robustness.
A smaller batch size and larger learning rate can lead to flatter minima in SAM, revealing a critical trade-off in hyperparameter tuning that impacts generalization.
