@inproceedings{wu-etal-2026-grove,
title = "Grove {M}o{E}: Towards Efficient and Superior {M}o{E} {LLM}s with Adjugate Experts",
author = "Wu, Haoyuan and
Chen, Haoxing and
Chen, Xiaodong and
Zhou, Zhanchao and
Chen, Tieyuan and
Zhuang, Yihong and
Lu, Guoshan and
Huang, Zenan and
Zhao, Junbo and
Liu, Lin and
Lan, Zhenzhong and
Yu, Bei and
Li, Jianguo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1668/",
pages = "35569--35581",
ISBN = "979-8-89176-462-0",
abstract = "The Mixture of Experts (MoE) architecture is a cornerstone of modern state-of-the-art (SOTA) large language models (LLMs). MoE models facilitate scalability by enabling sparse parameter activation. However, traditional MoE architecture uses homogeneous experts of a uniform size, activating a fixed number of parameters irrespective of input complexity and thus limiting computational efficiency. To overcome this limitation, we introduce Grove MoE, a novel architecture incorporating experts of varying sizes, inspired by the heterogeneous big.LITTLE CPU architecture. This architecture features novel adjugate experts with a dynamic activation mechanism, enabling model capacity expansion while maintaining manageable computational overhead. Building on this architecture, we present GroveMoE-Base and GroveMoE-Inst, 33B-parameter LLMs developed by applying an upcycling strategy to the Qwen3-30B-A3B-Base model during mid-training and post-training. GroveMoE models dynamically activate $3.14 \text{-} 3.28$B parameters based on token complexity and achieve performance comparable to SOTA open-source models of similar or even larger size."
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<abstract>The Mixture of Experts (MoE) architecture is a cornerstone of modern state-of-the-art (SOTA) large language models (LLMs). MoE models facilitate scalability by enabling sparse parameter activation. However, traditional MoE architecture uses homogeneous experts of a uniform size, activating a fixed number of parameters irrespective of input complexity and thus limiting computational efficiency. To overcome this limitation, we introduce Grove MoE, a novel architecture incorporating experts of varying sizes, inspired by the heterogeneous big.LITTLE CPU architecture. This architecture features novel adjugate experts with a dynamic activation mechanism, enabling model capacity expansion while maintaining manageable computational overhead. Building on this architecture, we present GroveMoE-Base and GroveMoE-Inst, 33B-parameter LLMs developed by applying an upcycling strategy to the Qwen3-30B-A3B-Base model during mid-training and post-training. GroveMoE models dynamically activate 3.14 \text- 3.28B parameters based on token complexity and achieve performance comparable to SOTA open-source models of similar or even larger size.</abstract>
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%0 Conference Proceedings
%T Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
%A Wu, Haoyuan
%A Chen, Haoxing
%A Chen, Xiaodong
%A Zhou, Zhanchao
%A Chen, Tieyuan
%A Zhuang, Yihong
%A Lu, Guoshan
%A Huang, Zenan
%A Zhao, Junbo
%A Liu, Lin
%A Lan, Zhenzhong
%A Yu, Bei
%A Li, Jianguo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wu-etal-2026-grove
%X The Mixture of Experts (MoE) architecture is a cornerstone of modern state-of-the-art (SOTA) large language models (LLMs). MoE models facilitate scalability by enabling sparse parameter activation. However, traditional MoE architecture uses homogeneous experts of a uniform size, activating a fixed number of parameters irrespective of input complexity and thus limiting computational efficiency. To overcome this limitation, we introduce Grove MoE, a novel architecture incorporating experts of varying sizes, inspired by the heterogeneous big.LITTLE CPU architecture. This architecture features novel adjugate experts with a dynamic activation mechanism, enabling model capacity expansion while maintaining manageable computational overhead. Building on this architecture, we present GroveMoE-Base and GroveMoE-Inst, 33B-parameter LLMs developed by applying an upcycling strategy to the Qwen3-30B-A3B-Base model during mid-training and post-training. GroveMoE models dynamically activate 3.14 \text- 3.28B parameters based on token complexity and achieve performance comparable to SOTA open-source models of similar or even larger size.
%U https://aclanthology.org/2026.findings-emnlp.1668/
%P 35569-35581
Markdown (Informal)
[Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts](https://aclanthology.org/2026.findings-emnlp.1668/) (Wu et al., Findings 2026)
ACL
- Haoyuan Wu, Haoxing Chen, Xiaodong Chen, Zhanchao Zhou, Tieyuan Chen, Yihong Zhuang, Guoshan Lu, Zenan Huang, Junbo Zhao, Lin Liu, Zhenzhong Lan, Bei Yu, and Jianguo Li. 2026. Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 35569–35581, Budapest, Hungary. Association for Computational Linguistics.