Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing
Fuente:
arXiv
Saved in:
| Main Authors: | Cong, Peizhuang, Yuan, Aomufei, Chen, Shimao, Tian, Yuxuan, Ye, Bowen, Yang, Tong |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Harder Tasks Need More Experts: Dynamic Routing in MoE Models
by: Huang, Quzhe, et al.
Published: (2024)
by: Huang, Quzhe, et al.
Published: (2024)
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)
Polysemantic Experts, Monosemantic Paths: Routing as Control in MoEs
by: Ye, Charles, et al.
Published: (2026)
by: Ye, Charles, et al.
Published: (2026)
Advancing Expert Specialization for Better MoE
by: Guo, Hongcan, et al.
Published: (2025)
by: Guo, Hongcan, et al.
Published: (2025)
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
by: Qian, Yulei, et al.
Published: (2024)
by: Qian, Yulei, et al.
Published: (2024)
MoE Lens -- An Expert Is All You Need
by: Chaudhari, Marmik, et al.
Published: (2026)
by: Chaudhari, Marmik, 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)
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)
MH-MoE: Multi-Head Mixture-of-Experts
by: Huang, Shaohan, et al.
Published: (2024)
by: Huang, Shaohan, et al.
Published: (2024)
Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training
by: Wang, Mengru, et al.
Published: (2025)
by: Wang, Mengru, et al.
Published: (2025)
LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training
by: Zhu, Tong, et al.
Published: (2024)
by: Zhu, Tong, et al.
Published: (2024)
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
by: Tang, Yehui, et al.
Published: (2025)
by: Tang, Yehui, et al.
Published: (2025)
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)
ExpertWeaver: Unlocking the Inherent MoE in Dense LLMs with GLU Activation Patterns
by: Zhao, Ziyu, et al.
Published: (2026)
by: Zhao, Ziyu, et al.
Published: (2026)
$μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
by: Koike-Akino, Toshiaki, 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)
DIVE into MoE: Diversity-Enhanced Reconstruction of Large Language Models from Dense into Mixture-of-Experts
by: Feng, Yuchen, et al.
Published: (2025)
by: Feng, Yuchen, et al.
Published: (2025)
Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts
by: Sun, Weigao, et al.
Published: (2025)
by: Sun, Weigao, et al.
Published: (2025)
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)
GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
by: Deng, Jianing, et al.
Published: (2026)
by: Deng, Jianing, et al.
Published: (2026)
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)
Post-Trained MoE Can Skip Half Experts via Self-Distillation
by: Lv, Xingtai, et al.
Published: (2026)
by: Lv, Xingtai, et al.
Published: (2026)
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)
SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation
by: Li, Zichong, et al.
Published: (2025)
by: Li, Zichong, et al.
Published: (2025)
SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts
by: Muzio, Alexandre, et al.
Published: (2024)
by: Muzio, Alexandre, et al.
Published: (2024)
Steering MoE LLMs via Expert (De)Activation
by: Fayyaz, Mohsen, et al.
Published: (2025)
by: Fayyaz, Mohsen, et al.
Published: (2025)
Do Domain-specific Experts exist in MoE-based LLMs?
by: Do, Giang, et al.
Published: (2026)
by: Do, Giang, et al.
Published: (2026)
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
by: Tang, Yehui, et al.
Published: (2025)
by: Tang, Yehui, et al.
Published: (2025)
GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors
by: Zhang, Hengyuan, et al.
Published: (2025)
by: Zhang, Hengyuan, et al.
Published: (2025)
Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression
by: Zhu, Peijun, et al.
Published: (2025)
by: Zhu, Peijun, et al.
Published: (2025)
Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, et al.
Published: (2025)
LLaDA-MoE: A Sparse MoE Diffusion Language Model
by: Zhu, Fengqi, et al.
Published: (2025)
by: Zhu, Fengqi, et al.
Published: (2025)
Expert Selections In MoE Models Reveal (Almost) As Much As Text
by: Nuriyev, Amir, et al.
Published: (2026)
by: Nuriyev, Amir, et al.
Published: (2026)
S2MoE: Robust Sparse Mixture of Experts via Stochastic Learning
by: Do, Giang, et al.
Published: (2025)
by: Do, Giang, et al.
Published: (2025)
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
by: Liu, Yang, et al.
Published: (2026)
by: Liu, Yang, 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)
Expert-Token Resonance MoE: Bidirectional Routing with Efficiency Affinity-Driven Active Selection
by: Li, Jing, et al.
Published: (2024)
by: Li, Jing, et al.
Published: (2024)
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
by: Shi, Chufan, et al.
Published: (2024)
by: Shi, Chufan, et al.
Published: (2024)
MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios
by: Li, Shuhuai, et al.
Published: (2026)
by: Li, Shuhuai, et al.
Published: (2026)
What Gets Activated: Uncovering Domain and Driver Experts in MoE Language Models
by: Hu, Guimin, et al.
Published: (2026)
by: Hu, Guimin, et al.
Published: (2026)
Similar Items
-
Harder Tasks Need More Experts: Dynamic Routing in MoE Models
by: Huang, Quzhe, et al.
Published: (2024) -
Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
by: Wu, Haoyuan, et al.
Published: (2025) -
Polysemantic Experts, Monosemantic Paths: Routing as Control in MoEs
by: Ye, Charles, et al.
Published: (2026) -
Advancing Expert Specialization for Better MoE
by: Guo, Hongcan, et al.
Published: (2025) -
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
by: Qian, Yulei, et al.
Published: (2024)