Cover Image

Mixtures of SubExperts for Large Language Continual Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Kang, Haeyong
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Upcycling Large Language Models into Mixture of Experts
by: He, Ethan, et al.
Published: (2024)

Soft-TransFormers for Continual Learning
by: Kang, Haeyong, et al.
Published: (2024)

Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
by: Lu, Xudong, et al.
Published: (2024)

FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models
by: Zhao, Zhongyu, et al.
Published: (2024)

Mixture of Heterogeneous Grouped Experts for Language Modeling
by: Ma, Zhicheng, et al.
Published: (2026)

LIBMoE: A Library for comprehensive benchmarking Mixture of Experts in Large Language Models
by: Nguyen, Nam V., et al.
Published: (2024)

Probing Semantic Routing in Large Mixture-of-Expert Models
by: Olson, Matthew Lyle, et al.
Published: (2025)

Pruning and Distilling Mixture-of-Experts into Dense Language Models
by: Kim, Junhyuck, et al.
Published: (2026)

OLMoE: Open Mixture-of-Experts Language Models
by: Muennighoff, Niklas, et al.
Published: (2024)

Every Expert Matters: Towards Effective Knowledge Distillation for Mixture-of-Experts Language Models
by: Kim, Gyeongman, et al.
Published: (2025)

Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
by: Nakamura, Taishi, et al.
Published: (2025)

Multilingual Routing in Mixture-of-Experts
by: Bandarkar, Lucas, et al.
Published: (2025)

Routing-Free Mixture-of-Experts
by: Liu, Yilun, et al.
Published: (2026)

Multi-Head Mixture-of-Experts
by: Wu, Xun, et al.
Published: (2024)

MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
by: Xie, Yanyue, et al.
Published: (2024)

Contrastive Learning and Mixture of Experts Enables Precise Vector Embeddings
by: Hallee, Logan, et al.
Published: (2024)

SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs?
by: Zhuang, Haomin, et al.
Published: (2024)

On the Spatial Structure of Mixture-of-Experts in Transformers
by: Bershatsky, Daniel, et al.
Published: (2025)

Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
by: Pan, Bowen, et al.
Published: (2024)

CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
by: Liu, Yang, et al.
Published: (2026)

Scaling Laws for Fine-Grained Mixture of Experts
by: Krajewski, Jakub, et al.
Published: (2024)

MoIN: Mixture of Introvert Experts to Upcycle an LLM
by: Tejankar, Ajinkya, et al.
Published: (2024)

Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
by: Han, Yixuan, et al.
Published: (2025)

Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules
by: Liu, Yilun, et al.
Published: (2025)

MoxE: Mixture of xLSTM Experts with Entropy-Aware Routing for Efficient Language Modeling
by: Thiombiano, Abdoul Majid O., et al.
Published: (2025)

Towards a Comprehensive Scaling Law of Mixture-of-Experts
by: Zhao, Guoliang, et al.
Published: (2025)

MobileMoE: Scaling On-Device Mixture of Experts
by: Chen, Yanbei, et al.
Published: (2026)

FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models
by: Jiang, Juyong, et al.
Published: (2026)

The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level
by: Herbst, Jeremy, et al.
Published: (2026)

LLMSurgeon: Diagnosing Data Mixture of Large Language Models
by: Luo, Yaxin, et al.
Published: (2026)

Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
by: Liu, Baihui, et al.
Published: (2026)

Analytic Subspace Routing: How Recursive Least Squares Works in Continual Learning of Large Language Model
by: Tong, Kai, et al.
Published: (2025)

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper
by: Zeng, Runjia, et al.
Published: (2025)

Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts
by: He, Shwai, et al.
Published: (2025)

Nexus: Specialization meets Adaptability for Efficiently Training Mixture of Experts
by: Gritsch, Nikolas, et al.
Published: (2024)

Continual Learning: Forget-free Winning Subnetworks for Video Representations
by: Kang, Haeyong, et al.
Published: (2023)

Learning Dynamics in Continual Pre-Training for Large Language Models
by: Wang, Xingjin, et al.
Published: (2025)

Continual Learning of Large Language Models: A Comprehensive Survey
by: Shi, Haizhou, et al.
Published: (2024)

MoECollab: Democratizing LLM Development Through Collaborative Mixture of Experts
by: Harshit
Published: (2025)

Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization
by: Nakamura, Taishi, et al.
Published: (2025)