Sparse Weights Blow Up

A wide MLP on 8k tabular rows drives training AUC to 0.99 while validation AUC stalls at 0.76. Feature semantics do not support label-preserving augmentation, and the largest weights sit on sparse one-hot inputs. Which regularization control should you try first?