[2606.07414] Sparsely gated tiny linear experts

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Computer Science > Machine Learning

arXiv:2606.07414 (cs)

[Submitted on 5 Jun 2026]

Title:Sparsely gated tiny linear experts

Authors:Simon Schug<br>View a PDF of the paper titled Sparsely gated tiny linear experts, by Simon Schug

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Abstract:Sparsity allows scaling model parameters without proportionally increasing computational cost. While mixture of experts (MoE) models are made increasingly sparse, individual experts typically remain large and dense. Here, we demonstrate that further increasing sparsity by shrinking each expert to consist of a single neuron and selecting a tiny fraction of many available neurons can improve compute efficiency and interpretability. Counterintuitively, the key to achieving both is removing the nonlinearity typically applied to the experts, resulting in a network of sparsely gated linear neurons (sgatlin). In an isoflop comparison, we find that replacing all transformer feedforward layers with sgatlin improves perplexity in language models across different compute budgets. At the same time, the sparsity and linearity of the resulting feedforward circuits present new opportunities for model interpretability. In a small-scale case study, we demonstrate that feedforward circuits in sgatlin can be interpreted without having to train additional replacement models. We find that they form semantically structured clusters and are causally implicated in factual recall. Our findings paint a possible path towards compute-efficient and interpretable transformer feedforward layers.

Comments:<br>Code available at this https URL

Subjects:

Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)

Cite as:<br>arXiv:2606.07414 [cs.LG]

(or<br>arXiv:2606.07414v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.07414

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Simon Schug [view email]<br>[v1]<br>Fri, 5 Jun 2026 16:06:47 UTC (1,095 KB)

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