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 ∞-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 ∞-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.
@article{arxiv.2601.17680,
title = {$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts},
author = {Shota Takashiro and Takeshi Kojima and Shohei Taniguchi and Yusuke Iwasawa and Yutaka Matsuo},
journal= {arXiv preprint arXiv:2601.17680},
year = {2026}
}