Summary of Remoe: Fully Differentiable Mixture-of-experts with Relu Routing, by Ziteng Wang et al.
ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing
by Ziteng Wang, Jun Zhu, Jianfei Chen
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes ReMoE, a novel Mixture-of-Experts (MoE) architecture that addresses the limitations of traditional TopK routers. By utilizing ReLU as the router instead of Softmax, ReMoE provides a fully differentiable and scalable solution for MoE models. The authors also introduce methods to regulate the router’s sparsity and balance the load among experts. Experimental results demonstrate that ReMoE consistently outperforms vanilla TopK-routed MoE across various model sizes, expert counts, and levels of granularity, while also exhibiting superior scalability with respect to the number of experts. Megatron-LM-based implementation is available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new type of computer model that can handle big tasks without using too much energy. The old way of making this kind of model was not very good because it wasn’t continuous, which means it couldn’t be changed easily as needed. So the researchers created a new way called ReMoE, which is like a special kind of manager that decides how to use the computer’s power. They tested it and found out that it works really well, even when they made the model bigger or added more parts. This is important because it could help make computers better at doing tasks that humans find hard. |
Keywords
» Artificial intelligence » Mixture of experts » Relu » Softmax
