Self-Routing: Parameter-Free Expert Routing from Hidden States
#Self-Routing #parameter-free #expert routing #hidden states #mixture-of-experts #neural networks #computational efficiency
📌 Key Takeaways
- Self-Routing introduces a parameter-free method for expert routing in neural networks.
- It leverages hidden states to determine routing decisions without additional trainable parameters.
- The approach aims to improve efficiency and scalability in mixture-of-experts models.
- It reduces computational overhead by eliminating the need for dedicated routing modules.
📖 Full Retelling
arXiv:2604.00421v1 Announce Type: new
Abstract: 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 in the MoE settings we study. We propose Self-Routing, a parameter-free routing mechanism that uses a designated subspace of the token hidden state directly as expert logits, eli
🏷️ Themes
Machine Learning, Neural Networks
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Original Source
arXiv:2604.00421v1 Announce Type: new
Abstract: 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 in the MoE settings we study. We propose Self-Routing, a parameter-free routing mechanism that uses a designated subspace of the token hidden state directly as expert logits, eli
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