When Data Imbalance Helps: Robust Generalization Through Shortcut Saturation
We study robust generalization under spurious correlations: tasks where a shortcut feature is correlated with the true label in training but anti-correlated in an adversarial held-out split. Varying the spurious ratio $r$ (the fraction of training examples where shortcut = true label) and model capacity, we find a counterintuitive result: data imbalance promotes generalization in sufficiently capable models. On a synthetic task where the true label is sum parity of an integer sequence and the shortcut is the parity of the maximum-valued element, a 2-layer, 2-head transformer generalized (reached $100\%$ adversarial accuracy) in 0% of seeds at $r{=}0.50$ but 77% of seeds at $r{=}0.90$. The...
| Authors | Cheng-Ting Chou, Duc Binh Hoang |
|---|---|
| Primary category | cs.LG |
| Published | 11 July 2026 |
| Last updated at source | 17 September 2026 |
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