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

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. The implementation based on Megatron-LM is available at https://github.com/thu-ml/ReMoE. Transformer models (Vaswani, 2017) consistently improve performance as the number of parameters increases (Kaplan et al., 2020). However, scaling these models is constrained by computation resources. Sparsely activated Mixture-of-Experts (MoE) (Shazeer et al., 2017) mitigates this challenge by employing a sparse architecture that selectively activates a subset of parameters during both training and inference.