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arXiv · 2601.21349

L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts

Minghao Yang·Ren Togo·Guang Li·Takahiro Ogawa·Miki Haseyama
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Abstract

Mixture-of-Experts (MoE) models scale neural networks by conditionally activating a small subset of experts, where the router plays a central role in determining expert specialization and overall model performance. However, many modern MoE systems still adopt linear routers in raw high-dimensional representation spaces, where representation mismatch, angular concentration, and scale-sensitive scoring can jointly undermine routing discriminability and stable expert specialization. In this work, we propose Low-rank & Lipschitz-controlled Routing (L2R), a unified routing framework that reshapes both the routing space and scoring geometry. L2R performs expert assignment in a shared low-rank latent routing space and introduces Saturated Inner-Product Scoring (SIPS) to explicitly control the Lipschitz behavior of routing functions, yielding smoother and more stable routing geometry. In addition, L2R incorporates a parameter-efficient multi-anchor routing mechanism to enhance expert expressiveness. Extensive experiments on an OLMoE-based language MoE model and a vision MoE setting on ImageNet demonstrate that L2R consistently improves routing geometry, expert discrimination, and overall model performance. Code will be released.

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Minghao Yang, Ren Togo, Guang Li, Takahiro Ogawa, Miki Haseyama. 2026-01-29. L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts. https://arxiv.org/abs/2601.21349

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