Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
Fuente:
arXiv
Saved in:
| Main Authors: | Jaiswal, Ajay, Wang, Jianyu, Li, Yixiao, Li, Pingzhi, Chen, Tianlong, Wang, Zhangyang, Wang, Chong, Pang, Ruoming, Du, Xianzhi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts
by: Luo, Shuqing, et al.
Published: (2024)
by: Luo, Shuqing, et al.
Published: (2024)
Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design
by: Zhang, Mohan, et al.
Published: (2025)
by: Zhang, Mohan, et al.
Published: (2025)
IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining
by: Li, Yixiao, et al.
Published: (2025)
by: Li, Yixiao, et al.
Published: (2025)
Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
by: Li, Pingzhi, et al.
Published: (2025)
by: Li, Pingzhi, et al.
Published: (2025)
Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference
by: Luo, Shuqing, et al.
Published: (2025)
by: Luo, Shuqing, et al.
Published: (2025)
Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training
by: Du, Xianzhi, et al.
Published: (2024)
by: Du, Xianzhi, et al.
Published: (2024)
QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
by: Li, Pingzhi, et al.
Published: (2024)
by: Li, Pingzhi, et al.
Published: (2024)
TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration
by: Wei, Linye, et al.
Published: (2026)
by: Wei, Linye, et al.
Published: (2026)
GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
by: Deng, Jianing, et al.
Published: (2026)
by: Deng, Jianing, et al.
Published: (2026)
EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware Optimization
by: Fu, Zhongqian, et al.
Published: (2025)
by: Fu, Zhongqian, et al.
Published: (2025)
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
by: Li, Yunxin, et al.
Published: (2024)
by: Li, Yunxin, et al.
Published: (2024)
MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing
by: Ma, Haiyue, et al.
Published: (2025)
by: Ma, Haiyue, et al.
Published: (2025)
OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
by: Shi, Jingze, et al.
Published: (2026)
by: Shi, Jingze, et al.
Published: (2026)
Polysemantic Experts, Monosemantic Paths: Routing as Control in MoEs
by: Ye, Charles, et al.
Published: (2026)
by: Ye, Charles, et al.
Published: (2026)
OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference
by: Wang, Liujianfu, et al.
Published: (2025)
by: Wang, Liujianfu, et al.
Published: (2025)
Compressing LLMs: The Truth is Rarely Pure and Never Simple
by: Jaiswal, Ajay, et al.
Published: (2023)
by: Jaiswal, Ajay, et al.
Published: (2023)
The Myth of Expert Specialization in MoEs: Why Routing Reflects Geometry, Not Necessarily Domain Expertise
by: Wang, Xi, et al.
Published: (2026)
by: Wang, Xi, et al.
Published: (2026)
DBES: A Systematic Benchmark and Metric Suite for Evaluating Expert Specialization in Large-Scale MoEs
by: Wang, Jing, et al.
Published: (2026)
by: Wang, Jing, et al.
Published: (2026)
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
by: Tang, Yehui, et al.
Published: (2025)
by: Tang, Yehui, et al.
Published: (2025)
Dirichlet-Prior Shaping: Guiding Expert Specialization in Upcycled MoEs
by: Mirvakhabova, Leyla, et al.
Published: (2025)
by: Mirvakhabova, Leyla, et al.
Published: (2025)
LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
by: Liu, Xinyi, et al.
Published: (2026)
by: Liu, Xinyi, et al.
Published: (2026)
Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging
by: Li, Lujun, et al.
Published: (2025)
by: Li, Lujun, et al.
Published: (2025)
TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts
by: Xu, Yu, et al.
Published: (2026)
by: Xu, Yu, et al.
Published: (2026)
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)
by: Jin, Peng, et al.
Published: (2024)
Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
by: Liu, Baihui, et al.
Published: (2026)
by: Liu, Baihui, et al.
Published: (2026)
$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
by: Chen, Guanjie, et al.
Published: (2024)
by: Chen, Guanjie, et al.
Published: (2024)
MoE-Loco: Mixture of Experts for Multitask Locomotion
by: Huang, Runhan, et al.
Published: (2025)
by: Huang, Runhan, et al.
Published: (2025)
I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts
by: Xin, Jiayi, et al.
Published: (2025)
by: Xin, Jiayi, et al.
Published: (2025)
Effective MoE-based LLM Compression by Exploiting Heterogeneous Inter-Group Experts Routing Frequency and Information Density
by: Mi, Zhendong, et al.
Published: (2026)
by: Mi, Zhendong, et al.
Published: (2026)
ECo-MoE: Embodiment-Conditioned Mixture of Experts Increases the Evolvability of Robots
by: Wang, Yibin, et al.
Published: (2026)
by: Wang, Yibin, et al.
Published: (2026)
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
by: Qian, Yulei, et al.
Published: (2024)
by: Qian, Yulei, et al.
Published: (2024)
UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training
by: Zheng, Size, et al.
Published: (2026)
by: Zheng, Size, et al.
Published: (2026)
$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts
by: Takashiro, Shota, et al.
Published: (2026)
by: Takashiro, Shota, et al.
Published: (2026)
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
by: Chen, Yuanteng, et al.
Published: (2025)
by: Chen, Yuanteng, et al.
Published: (2025)
AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference
by: Zhong, Shuzhang, et al.
Published: (2024)
by: Zhong, Shuzhang, et al.
Published: (2024)
MoE-SpeQ: Speculative Quantized Decoding with Proactive Expert Prefetching and Offloading for Mixture-of-Experts
by: Wang, Wenfeng, et al.
Published: (2025)
by: Wang, Wenfeng, et al.
Published: (2025)
MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs
by: Xia, Xinfeng, et al.
Published: (2025)
by: Xia, Xinfeng, et al.
Published: (2025)
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings
by: Farhat, Yehya, et al.
Published: (2023)
by: Farhat, Yehya, et al.
Published: (2023)
BuddyMoE: Exploiting Expert Redundancy to Accelerate Memory-Constrained Mixture-of-Experts Inference
by: Wang, Yun, et al.
Published: (2025)
by: Wang, Yun, et al.
Published: (2025)
Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
by: Wu, Haoyuan, et al.
Published: (2025)
by: Wu, Haoyuan, et al.
Published: (2025)
Similar Items
-
Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts
by: Luo, Shuqing, et al.
Published: (2024) -
Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design
by: Zhang, Mohan, et al.
Published: (2025) -
IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining
by: Li, Yixiao, et al.
Published: (2025) -
Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
by: Li, Pingzhi, et al.
Published: (2025) -
Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference
by: Luo, Shuqing, et al.
Published: (2025)