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arXiv · 2601.17680

$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts

Shota Takashiro·Takeshi Kojima·Shohei Taniguchi·Yusuke Iwasawa·Yutaka Matsuo
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Abstract

The Mixture of Experts (MoE) selects a few feed-forward networks (FFNs) per token, achieving an effective trade-off between computational cost and performance. In conventional MoE, each expert is treated as entirely independent, and experts are combined in a discrete space. As a result, when the number of experts increases, it becomes difficult to train each expert effectively. To stabilize training while increasing the number of experts, we propose $\infty$-MoE that selects a portion of the parameters of large FFNs based on continuous values sampled for each token. By considering experts in a continuous space, this approach allows for an infinite number of experts while maintaining computational efficiency. Experiments show that a GPT-2 Small-based $\infty$-MoE model, with 129M active and 186M total parameters, achieves comparable performance to a dense GPT-2 Medium with 350M parameters. Adjusting the number of sampled experts at inference time allows for a flexible trade-off between accuracy and speed, with an improvement of up to 2.5\% in accuracy over conventional MoE.

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Shota Takashiro, Takeshi Kojima, Shohei Taniguchi, Yusuke Iwasawa, Yutaka Matsuo. 2026-01-25. $\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts. https://arxiv.org/abs/2601.17680

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