Rethinking Routing Exploration in Modern Mixture-of-Experts

Abstract:Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two design choices: fine-grained expert segmentation and shared experts. The former increases expressiveness by enabling more expert combinations at a fixed activation ratio, and the latter introduces experts that are active for every token. We identify an exploration dilemma between these designs: shared experts can make routing less exploratory by reducing the variability of token representations. This conflicts with the greater need for exploration induced by fine-grained expert segmentation due to the combinatorially enlarged routing space. Consequently, the additional expressiveness offered by fine-grained segmentation may not be fully realized in practice. MirrorMoE is proposed to reconcile this exploration dilemma by shrinking the exploration space through token-adaptive suppression of unnecessary experts. MirrorMoE consistently improves performance across model scales.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.22353 [cs.LG]

(or arXiv:2610.22353v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.22353 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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