@inproceedings{liu-etal-2026-routing,
title = "Routing-Free Mixture of Experts",
author = "Liu, Yilun and
Han, Jinru and
Yan, Sikuan and
Tresp, Volker and
Ma, Yunpu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.579/",
pages = "13253--13271",
ISBN = "979-8-89176-459-0",
abstract = "Standard Mixture-of-Experts (MoE) designs rely heavily on centralized routing mechanisms that introduce rigid inductive biases. We propose Routing-Free MoE, which eliminates all hard-coded centralized components, including external routers, Softmax, TopK, and fixed load balancing. All related functionalities become encapsulated within individual experts, allowing each expert to determine its activation entirely on its own. We introduce a unified, adaptively scheduled load-balancing framework to simultaneously optimize both expert-balancing and token-balancing objectives through a customizable interpolation, letting optimal resource allocation pattern emerge locally during training. Extensive experiments demonstrate that Routing-Free MoE achieves consistently better performance, efficiency, and robustness than baseline approaches at both training and deployment. We further analyze its emergent behavior in detail and offer insights that may facilitate future MoE design and optimization. Code is available at https://liuyilun2000.github.io/RoutingFreeMoE/."
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<abstract>Standard Mixture-of-Experts (MoE) designs rely heavily on centralized routing mechanisms that introduce rigid inductive biases. We propose Routing-Free MoE, which eliminates all hard-coded centralized components, including external routers, Softmax, TopK, and fixed load balancing. All related functionalities become encapsulated within individual experts, allowing each expert to determine its activation entirely on its own. We introduce a unified, adaptively scheduled load-balancing framework to simultaneously optimize both expert-balancing and token-balancing objectives through a customizable interpolation, letting optimal resource allocation pattern emerge locally during training. Extensive experiments demonstrate that Routing-Free MoE achieves consistently better performance, efficiency, and robustness than baseline approaches at both training and deployment. We further analyze its emergent behavior in detail and offer insights that may facilitate future MoE design and optimization. Code is available at https://liuyilun2000.github.io/RoutingFreeMoE/.</abstract>
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%0 Conference Proceedings
%T Routing-Free Mixture of Experts
%A Liu, Yilun
%A Han, Jinru
%A Yan, Sikuan
%A Tresp, Volker
%A Ma, Yunpu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-routing
%X Standard Mixture-of-Experts (MoE) designs rely heavily on centralized routing mechanisms that introduce rigid inductive biases. We propose Routing-Free MoE, which eliminates all hard-coded centralized components, including external routers, Softmax, TopK, and fixed load balancing. All related functionalities become encapsulated within individual experts, allowing each expert to determine its activation entirely on its own. We introduce a unified, adaptively scheduled load-balancing framework to simultaneously optimize both expert-balancing and token-balancing objectives through a customizable interpolation, letting optimal resource allocation pattern emerge locally during training. Extensive experiments demonstrate that Routing-Free MoE achieves consistently better performance, efficiency, and robustness than baseline approaches at both training and deployment. We further analyze its emergent behavior in detail and offer insights that may facilitate future MoE design and optimization. Code is available at https://liuyilun2000.github.io/RoutingFreeMoE/.
%U https://aclanthology.org/2026.emnlp-main.579/
%P 13253-13271
Markdown (Informal)
[Routing-Free Mixture of Experts](https://aclanthology.org/2026.emnlp-main.579/) (Liu et al., EMNLP 2026)
ACL
- Yilun Liu, Jinru Han, Sikuan Yan, Volker Tresp, and Yunpu Ma. 2026. Routing-Free Mixture of Experts. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 13253–13271, Budapest, Hungary. Association for Computational Linguistics.