Generalization and Scaling Laws for Mixture-of-Experts Transformers
We develop a theory of generalization and scaling for Mixture-of-Experts (MoE) Transformers that cleanly separates \emph{active} per-input capacity from routing combinatorics. By conditioning on fixed routing patterns and union-bounding across them, we derive a sup-norm covering-number bound whose metric entropy scales with the active parameter budget and incurs a MoE-specific routing overhead. Combined with a standard ERM analysis for squared loss, this yields a generalization bound under a $d$-dimensional manifold data model and $C^β$ targets, showing that approximation and estimation trade off as in dense networks once active parameters are accounted for appropriately. We further prove...
| Authors | Mansour Zoubeirou a Mayaki |
|---|---|
| Primary category | cs.LG |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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