@inproceedings{takashiro-etal-2026-infinity,
title = "Infinity-{M}o{E}: Generalizing Mixture of Experts to Infinite Experts",
author = "Takashiro, Shota and
Kojima, Takeshi and
Taniguchi, Shohei and
Iwasawa, Yusuke and
Matsuo, Yutaka",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-short.33/",
pages = "448--456",
ISBN = "979-8-89176-381-4",
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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<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 ınfty-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 ınfty-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.</abstract>
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%0 Conference Proceedings
%T Infinity-MoE: Generalizing Mixture of Experts to Infinite Experts
%A Takashiro, Shota
%A Kojima, Takeshi
%A Taniguchi, Shohei
%A Iwasawa, Yusuke
%A Matsuo, Yutaka
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-381-4
%F takashiro-etal-2026-infinity
%X 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 ınfty-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 ınfty-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.
%U https://aclanthology.org/2026.eacl-short.33/
%P 448-456
Markdown (Informal)
[Infinity-MoE: Generalizing Mixture of Experts to Infinite Experts](https://aclanthology.org/2026.eacl-short.33/) (Takashiro et al., EACL 2026)
ACL
- Shota Takashiro, Takeshi Kojima, Shohei Taniguchi, Yusuke Iwasawa, and Yutaka Matsuo. 2026. Infinity-MoE: Generalizing Mixture of Experts to Infinite Experts. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 448–456, Rabat, Morocco. Association for Computational Linguistics.