ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

ICLR 2025 Conference Submission13061 Authors

Keywords: Mixture-of-Experts, Differentiable Routing, Sparsity

TL;DR: We propose ReMoE, a fully differentiable MoE with ReLU routing. ReMoE consistently outperforms vanilla TopK-routed MoE and exhibits superior scalability w.r.t. the number of experts.

Abstract:

Sparsely activated Mixture-of-Experts (MoE) models are widely adopted to scale up model capacity without increasing the computation budget. However, vanilla TopK routers are trained in a discontinuous, non-differentiable way, limiting their performance and scalability.
To address this issue, we propose ReMoE, a fully differentiable MoE architecture that offers a simple yet effective drop-in replacement for the conventional TopK+Softmax routing, utilizing ReLU as the router instead. We further propose methods to regulate the router's sparsity while balancing the load among experts. ReMoE’s continuous nature enables efficient dynamic allocation of computation across tokens and layers, while also exhibiting domain specialization. Our experiments demonstrate that ReMoE consistently outperforms vanilla TopK-routed MoE across various model sizes, expert counts, and levels of granularity. Furthermore, ReMoE exhibits superior scalability with respect to the number of experts, surpassing traditional MoE architectures.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 13061