From Pseudo-Balancing to True Specialization: Memory-Aware Routing for Mixture-of-Experts (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to optimize expert-centered load balancing fail to account for pseudo-balance phenomenon . severe knowledge overlap among experts leads to redundant representations and inefficient parameter utilization . |
| Approach: | They propose a method that prioritizes expert utilization over semantic alignment . they use memory-aware routing to ensure expert load balancing is consistent . |
| Outcome: | Experimental results show that MAR improves expert specialization by 35% and accuracy by 2%-25% . MAR matches baseline performance with only half the experts . |
Similar Papers
A Closer Look into Mixture-of-Experts in Large Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Mixture-of-experts (MoE) architectures are gaining increasing attention for their unique properties and remarkable performance. |
| Approach: | They propose a mixture-of-experts architecture that allows for model scaling without sacrificing computational efficiency. |
| Outcome: | The proposed model increases model size without sacrificing computational efficiency . the proposed model is modular and can be used by a broad spectrum of practitioners . |
Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models (2025.acl-long)
Copied to clipboard
Zihan Qiu, Zeyu Huang, Bo Zheng, Kaiyue Wen, Zekun Wang, Rui Men, Ivan Titov, Dayiheng Liu, Jingren Zhou, Junyang Lin
| Challenge: | Existing Mixture-of-Experts training frameworks use a micro-batch to calculate LBL . micro-batches are restricted to a single sequence, preventing expert specialization . |
| Approach: | They propose to use a global-batch to loosen the load balance constraint for MoEs models . they propose to synchronize fi across micro-batches and then use it to calculate the LBL . |
| Outcome: | The proposed global-batch LBL improves the domain specialization of experts . the micro-battery LBL is almost at the sequence level, and the router is pushed to distribute the token evenly . |
Mixture-of-Clustered-Experts: Advancing Expert Specialization and Generalization in Instruction Tuning (2025.emnlp-main)
Copied to clipboard
| Challenge: | A sparse Mixture-of-Experts architecture has emerged as a highly scalable solution for instruction tuning. |
| Approach: | They propose a mixture-of-Clustered-Experts (MoCE) architecture that allows expert specialization . they evaluate the mechanism on a set of benchmarks and show its superiority . |
| Outcome: | The proposed approach outperforms existing models and benchmarks on instruction tuning scenarios with significant input heterogeneity. |
StableMoE: Stable Routing Strategy for Mixture of Experts (2022.acl-long)
Copied to clipboard
| Challenge: | Existing learning-to-route methods suffer from the routing fluctuation issue . with the model scale growing, training speed will go slower and memory requirements are heavy . |
| Approach: | They propose a Mixture-of-Experts technique that can scale up the model size of Transformers with an affordable computational overhead. |
| Outcome: | The proposed method outperforms existing learning-to-route methods on language modeling and multilingual machine translation. |
Bag of Tricks for Sparse Mixture-of-Experts: A Benchmark Across Reasoning, Efficiency, and Safety (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing benchmarks focus on isolated aspects of MoE, with conflicting conclusions . a lack of consensus on optimal design choices is limiting to specific aspects of the model. |
| Approach: | They propose to evaluate two popular MoE backbones across four dimensions of design choices . they find token-level routing and z-loss regularization improve reasoning performance . |
| Outcome: | The proposed framework evaluates two popular MoE backbones on over eight metrics. |
Dynamic Expert Specialization: Towards Catastrophic Forgetting-Free Multi-Domain MoE Adaptation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to adapt Mixture-of-Experts models to multiple domains are prohibitive computation, cross-domain interference or require separate runs per domain. |
| Approach: | They propose a dynamic expert specialization framework for multi-domain adaptation of Mixture-of-Experts models. |
| Outcome: | The proposed framework reduces forgetting by 89% compared to full fine-tuning as domains scale from 2 to 6 and achieves faster convergence than conventional methods. |
Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts (2025.naacl-long)
Copied to clipboard
| Challenge: | Mixture-of-Experts (MoE) models are constrained by their fixed model capacities when the number of tasks grows in instruction tuning. |
| Approach: | They propose to combine all training tasks and apply fixed sampling weights without considering the importance of different tasks as the model training state changes. |
| Outcome: | The proposed method can be used on knowledge & reasoning tasks and open-ended queries with limited training budget. |
Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design (2025.naacl-long)
Copied to clipboard
| Challenge: | Using Mixture-of-Experts, researchers have found that efficient MoE is difficult to achieve due to two key reasons: imbalanced expert activation and massive communication overhead. |
| Approach: | They propose a collaboration-constrained routing strategy that encourages more specialized expert groups and leverages expert specialization. |
| Outcome: | The proposed approach achieves an average performance improvement of 0.51% and 0.33% on LLaMA-MoE and Qwen-MaE respectively. |
Probing Semantic Routing in Large Mixture-of-Expert Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | large mixture-of-expert models have become increasingly common in the open domain . prior work has explored functional differentiation through routing behavior . |
| Approach: | They investigate whether expert routing in large mixture-of-expert models is influenced by the semantics of the inputs. |
| Outcome: | The results show that expert routing is influenced by the semantics of the inputs. |
Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have outstanding performance by learning a large number of model parameters on large amounts of data. |
| Approach: | They propose a method of grouping and pruning similar experts to improve the model’s parameter efficiency by a range of natural language tasks. |
| Outcome: | The proposed method outperforms other model pruning methods on a range of natural language tasks. |