Self-Routing: Parameter-Free Expert Routing from Hidden States
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, eliminating the router projection entirely while leaving the rest of the MoE layer unchanged. We evaluate Self-Routing on GPT-2-scale language modeling and ImageNet-1K classification by comparing it against a standard learned router, random-routing baselines, and dense non-MoE baselines. Our results show that Self-Routing remains competitive with the learned-router baseline while removing all dedicated routing parameters, and yields more balanced expert utilization, with about 17 % higher average n
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, eliminating the router projection entirely while leaving the rest of the MoE layer unchanged. We evaluate Self-Routing on GPT-2-scale language modeling and ImageNet-1K classification by comparing it against a standard learned router, random-routing baselines, and dense non-MoE baselines. Our results show that Self-Routing remains competitive with the learned-router baseline while removing all dedicated routing parameters, and yields more balanced expert utilization, with about 17 % higher average normalized routing entropy and no explicit load-balancing loss. On ImageNet-1K with DeiT-S/16, Self-Routing also slightly improves over the corresponding learned-router MoE. These findings suggest that effective MoE routing can emerge from the hidden representation itself without requiring a separate learned router module.
Executive Summary
This article introduces Self-Routing, a parameter-free expert routing mechanism that eliminates the need for a dedicated learned router in Mixture-of-Experts (MoE) layers. Self-Routing leverages a designated subspace of the token hidden state directly as expert logits, demonstrating competitive performance with a standard learned router on GPT-2-scale language modeling and ImageNet-1K classification tasks. The results show improved balanced expert utilization and increased normalized routing entropy, suggesting that effective MoE routing can emerge from the hidden representation itself without requiring a separate learned router module.
Key Points
- ▸ Self-Routing eliminates the need for a dedicated learned router in MoE layers.
- ▸ Parameter-free routing mechanism uses a designated subspace of the token hidden state as expert logits.
- ▸ Competitive performance with a standard learned router on GPT-2-scale language modeling and ImageNet-1K classification tasks.
Merits
Strength
Self-Routing demonstrates competitive performance with a standard learned router, suggesting that effective MoE routing can emerge from the hidden representation itself.
Improved Expert Utilization
Self-Routing yields more balanced expert utilization, with about 17% higher average normalized routing entropy.
Demerits
Dependence on Hidden Representation
Self-Routing relies on the quality and representativeness of the hidden state, which may not always be reliable.
Expert Commentary
While Self-Routing demonstrates promising results, it is essential to consider the potential limitations and dependencies on the quality of the hidden representation. The study's findings suggest that effective MoE routing can emerge from the hidden representation itself, but further research is needed to fully understand the implications of this discovery. Self-Routing's parameter-free approach may lead to more efficient and scalable MoE implementations, but it is crucial to evaluate its performance in various tasks and scenarios to ensure its effectiveness. Additionally, the study's results may have broader implications for the development of future AI models and the efficient use of resources in AI systems.
Recommendations
- ✓ Future research should focus on exploring the potential of Self-Routing in other areas of AI, such as computer vision and natural language processing.
- ✓ Investigating the robustness and reliability of Self-Routing across various tasks and datasets is essential to ensure its effectiveness and widespread adoption.
Sources
Original: arXiv - cs.AI
