aiqbench analysis
STAR addresses the instability of standard Mixture-of-Experts routing by integrating an evolving principal subspace via the Generalized Hebbian Algorithm to better track input structure. This approach yields more stable expert specialization and improved generalization under distribution shifts compared to shallow linear projection baselines. The method demonstrates consistent performance gains across large-scale language and vision tasks, offering a robust alternative for scaling MoE architectures.
STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning
arXiv:2606.08814v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) scales model capacity efficiently by selectively routing inputs to a specialized subset of experts. However, input-expert specialization, the core motivation of MoE, critically depends on whether the router is actually aware of input structure. In practice, MoE routing is typically implemented as a shallow linear projection with limited awareness of input representation, which often leads to unstable routing. We propose STAR, a Structure Aware Routing that rethinks MoE routing as a subspace learning problem by augmenting standard learnable routing with an evolving principal subspace that tracks dominant input structure via Generalized Hebbian Algorithm (GHA). By aligning routing decisions directly with input structure, STAR enables stable expert specialization. We evaluate STAR on controlled synthetic setup and large-scale language and vision tasks, where it consistently improves routing quality and downstream performance over strong MoE baselines. Moreover, optional test-time subspace updates further enhance routing robustness and generalization under input distribution shifts.
Abstract:Mixture-of-Experts (MoE) scales model capacity efficiently by selectively routing inputs to a specialized subset of experts. However, input-expert specialization, the core motivation of MoE, critically depends on whether the router is actually aware of input structure. In practice, MoE routing is typically implemented as a shallow linear projection with limited awareness of input representation, which often leads to unstable routing. We propose STAR, a Structure Aware Routing that rethinks MoE routing as a subspace learning problem by augmenting standard learnable routing with an evolving principal subspace that tracks dominant input structure via Generalized Hebbian Algorithm (GHA). By aligning routing decisions directly with input structure, STAR enables stable expert specialization. We evaluate STAR on controlled synthetic setup and large-scale language and vision tasks, where it consistently improves routing quality and downstream performance over strong MoE baselines. Moreover, optional test-time subspace updates further enhance routing robustness and generalization under input distribution shifts.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2606.08814 [cs.AI]
(or arXiv:2606.08814v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.08814 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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