When Does Sparse Routing Help? The Geometry of Expert Separation in Mixture of Experts

Title:When Does Sparse Routing Help? The Geometry of Expert Separation in Mixture of Experts

Abstract:Sparse mixtures of experts combine multiple prediction models while evaluating only a small subset for each input. Although this architecture reduces computation, how expert separation and routing sparsity jointly govern expert recovery remains an open problem. In this paper, we address this question for location Gaussian mixtures of experts with a fixed number of experts by establishing how expert separation and spatial arrangement determine recovery rates. For dense models, our results reveal that when experts form well-separated clusters, the separation geometry is governed by the size of the largest cluster; without a prescribed cluster structure, this geometry is instead driven by the total number of experts. For sparse models, the separation geometry on retained routing regions is determined by the number of active experts, while the contribution of omitted regions is controlled by their total probability. Consequently, activating fewer experts reduces the separation complexity in these bounds. Furthermore, Top-1 routing yields a particularly striking result: experts can be recovered at a rate independent of the separation quantity. Our technical approach combines polynomial interpolation and Gaussian reproducing-kernel Hilbert space (RKHS) test functions with quantitative restriction inequalities that extend pointwise comparisons to expert functions.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2611.01602 [stat.ML]

(or arXiv:2611.01602v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2611.01602 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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