Title: Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss
Primary area: foundation or frontier models, including LLMs
Keywords: Mixture-of-Experts, Large language models, Auxiliary loss, Expert-router coupling, Expert specialization

Abstract: Traditional Mixture-of-Experts (MoE) models lack explicit constraints to ensure the router’s decisions align well with the experts’ capabilities, which ultimately limits model performance. To address this, we propose expert-router coupling loss (ERC loss), a lightweight auxiliary loss that couples expert capabilities and the router’s decisions. We treat each row of the router matrix as a cluster center for the tokens assigned to a particular expert. From these centers, we create proxy tokens by applying a perturbation with noise. Using these proxy tokens, the ERC loss forces the router and experts to satisfy two constraints: (1) each expert exhibits higher activation for its corresponding proxy token than for any other proxy token, and (2) each proxy token elicits stronger activation in its designated expert than in any other expert. This optimization leads to two key effects: each row of the router matrix is an accurate representation of its expert’s capabilities, while each expert develops expertise that closely match the tokens routed to it. Our experiments involve pre-training multiple 3B-parameter MoE-LLMs on trillions of tokens in total, providing detailed evidence of the ERC loss’s effectiveness. Additionally, the ERC loss offers flexible control and quantitative tracking of expert specialization levels during training, providing many valuable insights into MoEs.