Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design

Mohan Zhang, Pingzhi Li, Jie Peng, Mufan Qiu, Tianlong Chen


Anthology ID:
2025.naacl-long.347
Volume:
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
April
Year:
2025
Address:
Albuquerque, New Mexico
Editors:
Luis Chiruzzo, Alan Ritter, Lu Wang
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6815–6825
Language:
URL:
https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.347/
DOI:
Bibkey:
Cite (ACL):
Mohan Zhang, Pingzhi Li, Jie Peng, Mufan Qiu, and Tianlong Chen. 2025. Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 6815–6825, Albuquerque, New Mexico. Association for Computational Linguistics.
Cite (Informal):
Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design (Zhang et al., NAACL 2025)
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PDF:
https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.347.pdf