@inproceedings{wang-etal-2025-hmoe,
title = "{HM}o{E}: Heterogeneous Mixture of Experts for Language Modeling",
author = "Wang, An and
Sun, Xingwu and
Xie, Ruobing and
Li, Shuaipeng and
Zhu, Jiaqi and
Yang, Zhen and
Zhao, Pinxue and
Han, Weidong and
Kang, Zhanhui and
Wang, Di and
Okazaki, Naoaki and
Xu, Cheng-zhong",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1115/",
doi = "10.18653/v1/2025.emnlp-main.1115",
pages = "21943--21957",
ISBN = "979-8-89176-332-6",
abstract = "Mixture of Experts (MoE) offers remarkable performance and computational efficiency by selectively activating subsets of model parameters. Traditionally, MoE models use homogeneous experts, each with identical capacity. However, varying complexity in input data necessitates experts with diverse capabilities, while homogeneous MoE hinders effective expert specialization and efficient parameter utilization. In this study, we propose a novel Heterogeneous Mixture of Experts (HMoE) framework, where experts differ in size and thus possess diverse capacities. This heterogeneity allows for more specialized experts to handle varying token complexities more effectively. To address the imbalance in expert activation, we propose a novel training objective that encourages the frequent activation of smaller experts, so as to improve computational efficiency and parameter utilization. Extensive experiments demonstrate that HMoE achieves a lower loss rate with fewer activated parameters and outperforms conventional homogeneous MoE models on various pre-training evaluation benchmarks. Codes will be released upon acceptance."
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<abstract>Mixture of Experts (MoE) offers remarkable performance and computational efficiency by selectively activating subsets of model parameters. Traditionally, MoE models use homogeneous experts, each with identical capacity. However, varying complexity in input data necessitates experts with diverse capabilities, while homogeneous MoE hinders effective expert specialization and efficient parameter utilization. In this study, we propose a novel Heterogeneous Mixture of Experts (HMoE) framework, where experts differ in size and thus possess diverse capacities. This heterogeneity allows for more specialized experts to handle varying token complexities more effectively. To address the imbalance in expert activation, we propose a novel training objective that encourages the frequent activation of smaller experts, so as to improve computational efficiency and parameter utilization. Extensive experiments demonstrate that HMoE achieves a lower loss rate with fewer activated parameters and outperforms conventional homogeneous MoE models on various pre-training evaluation benchmarks. Codes will be released upon acceptance.</abstract>
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%0 Conference Proceedings
%T HMoE: Heterogeneous Mixture of Experts for Language Modeling
%A Wang, An
%A Sun, Xingwu
%A Xie, Ruobing
%A Li, Shuaipeng
%A Zhu, Jiaqi
%A Yang, Zhen
%A Zhao, Pinxue
%A Han, Weidong
%A Kang, Zhanhui
%A Wang, Di
%A Okazaki, Naoaki
%A Xu, Cheng-zhong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F wang-etal-2025-hmoe
%X Mixture of Experts (MoE) offers remarkable performance and computational efficiency by selectively activating subsets of model parameters. Traditionally, MoE models use homogeneous experts, each with identical capacity. However, varying complexity in input data necessitates experts with diverse capabilities, while homogeneous MoE hinders effective expert specialization and efficient parameter utilization. In this study, we propose a novel Heterogeneous Mixture of Experts (HMoE) framework, where experts differ in size and thus possess diverse capacities. This heterogeneity allows for more specialized experts to handle varying token complexities more effectively. To address the imbalance in expert activation, we propose a novel training objective that encourages the frequent activation of smaller experts, so as to improve computational efficiency and parameter utilization. Extensive experiments demonstrate that HMoE achieves a lower loss rate with fewer activated parameters and outperforms conventional homogeneous MoE models on various pre-training evaluation benchmarks. Codes will be released upon acceptance.
%R 10.18653/v1/2025.emnlp-main.1115
%U https://aclanthology.org/2025.emnlp-main.1115/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1115
%P 21943-21957
Markdown (Informal)
[HMoE: Heterogeneous Mixture of Experts for Language Modeling](https://aclanthology.org/2025.emnlp-main.1115/) (Wang et al., EMNLP 2025)
ACL
- An Wang, Xingwu Sun, Ruobing Xie, Shuaipeng Li, Jiaqi Zhu, Zhen Yang, Pinxue Zhao, Weidong Han, Zhanhui Kang, Di Wang, Naoaki Okazaki, and Cheng-zhong Xu. 2025. HMoE: Heterogeneous Mixture of Experts for Language Modeling. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 21943–21957, Suzhou, China. Association for Computational Linguistics.