Towards a Statistical Understanding of Mixture-of-Experts

2026-09-03

AI Summary

本文通过局部聚合视角分析混合专家模型，探讨路由、稀疏激活和共享专家等设计选择的统计作用，分离出逼近误差、专家学习误差和路由器估计误差。

Abstract

Mixture-of-experts (MoE) architectures increase model capacity by combining a collection of expert predictors through input-dependent routing, while often activating only a small subset of experts for each input. Despite their growing importance in modern large-scale models, the statistical roles of their design choices, especially routing, sparse activation, and shared experts, remain only partially understood, as existing theory has largely focused on parametric or correctly specified MoE models. In this paper, we view MoE as a form of localized aggregation and show how this localization reshapes the approximation-estimation-computation tradeoff. We derive oracle risk bounds for learning dense and sparse routing with evolving experts, separating approximation, expert-learning, and router-estimation errors, and characterize how sparse Top-K routing can retain the benefits of localized aggregation while controlling per-input computation. We also interpret gating through the geometry of input space, relating routing performance to regions of local expert advantage, and show how shared experts, as adopted in architectures such as DeepSeekMoE, can extract common predictive structure so that routed experts focus on residual local variation. Together, these results provide a unified statistical framework for understanding MoE through input-dependent expert aggregation, in which expert specialization and computational tradeoffs are governed by local predictive structure.

Problem

• Mixture-of-Experts
• routing
• sparse activation
• shared experts
• localization

Innovation

• Localized Aggregation
• Sparse Top-K Routing
• Input-Dependent Expert Aggregation
• Shared Experts