Self-Routing: Parameter-Free Expert Routing from Hidden States
Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely on a learned router to map hidden states to expert assignments. In this work, we ask whether a dedicated learned router is strictly necessary for MoE routing. We propose Self-Routing, a parameter-free routing mechanism that uses a designated subspace of the token hidden state directly as expert logits, eliminating the router projection entirely while leaving the rest of the MoE layer unchanged. We evaluate Self-Routing on language modeling across different expert counts and model scales, and on ImageNet-1K classification by comparing it against a standard lear...
| Authors | Jama Hussein Mohamud, Drew Wagner, Mirco Ravanelli |
|---|---|
| Primary category | cs.AI |
| Published | 1 April 2026 |
| Last updated at source | 7 August 2026 |
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