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arXiv · 2601.14792

Robustness of Mixtures of Experts to Feature Noise

Dong Sun·Rahul Nittala·Rebekka Burkholz
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Abstract

Despite their practical success, it remains unclear why Mixture of Experts (MoE) models can outperform dense networks beyond sheer parameter scaling. We study an iso-parameter regime where inputs exhibit latent modular structure but are corrupted by feature noise, a proxy for noisy internal activations. We show that sparse expert activation acts as a noise filter: compared to a dense estimator, MoEs achieve lower generalization error under feature noise, improved robustness to perturbations, and faster convergence speed. Empirical results on synthetic data and real-world language tasks corroborate the theoretical insights, demonstrating consistent robustness and efficiency gains from sparse modular computation.

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Dong Sun, Rahul Nittala, Rebekka Burkholz. 2026-01-21. Robustness of Mixtures of Experts to Feature Noise. https://arxiv.org/abs/2601.14792

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