| 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. |
Similar Papers
On the Benefits of Learning to Route in Mixture-of-Experts Models (2023.emnlp-main)
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| Challenge: | Existing Mixture-of-Expert (MoE) models allow us to scale up model sizes while keeping the amount of compute time fixed. |
| Approach: | They propose to use a router to route inputs to experts in a layer to scale up model sizes while keeping the amount of compute time fixed. |
| Outcome: | The proposed model scales up with the help of a router that routes input tokens to experts in a layer and shows that it is more efficient than a non-trainable router. |
Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules (2026.findings-eacl)
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| Challenge: | Existing Parameter-Efficient Fine-Tuning (PEFT) strategies that focus on specialized experts are not effective for Mixture-of-Experts (MoE). |
| Approach: | They propose to integrate a dynamic routing mechanism among specialized experts in Mixture-of-Experts (MoE) . |
| Outcome: | Extensive experiments on commonsense and math reasoning tasks validate the performance and efficiency of the proposed routed approach. |
A Closer Look into Mixture-of-Experts in Large Language Models (2025.findings-naacl)
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| 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 . |
From Pseudo-Balancing to True Specialization: Memory-Aware Routing for Mixture-of-Experts (2026.findings-acl)
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| 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 . |
Bag of Tricks for Sparse Mixture-of-Experts: A Benchmark Across Reasoning, Efficiency, and Safety (2025.findings-emnlp)
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| 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. |
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation (2023.findings-acl)
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Ganesh Jawahar, Subhabrata Mukherjee, Xiaodong Liu, Young Jin Kim, Muhammad Abdul-Mageed, Laks Lakshmanan, V.S., Ahmed Hassan Awadallah, Sebastien Bubeck, Jianfeng Gao
| Challenge: | Existing MoE designs do not consider computational constraints (e.g., FLOPs, latency) Existing works in MoE consider homogeneous design where the same number of experts of the same size are placed uniformly throughout the network. |
| Approach: | They propose a framework for designing heterogeneous MoEs under computational constraints. |
| Outcome: | The proposed framework achieves 4x inference speedup and FLOPs reduction over manual models and within 1 BLEU point of MoE SwitchTransformer over benchmark datasets for NMT. |
HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts (2024.acl-long)
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| Challenge: | Existing methods for enhancing performance through increased use of expert knowledge often result in diminishing sparsity during expert selection. |
| Approach: | They propose a framework that integrates the computational processes of MoE with the concept of knowledge transferring in multi-task learning. |
| Outcome: | The proposed framework outperforms existing methods under identical conditions concerning the number of experts. |
XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection (2024.findings-acl)
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| Challenge: | XMoE leverages small experts and a threshold-based router to selectively engage only essential parameters. |
| Approach: | They propose a novel MoE that leverages small experts to selectively engage only essential parameters. |
| Outcome: | The proposed model can reduce computation load at MoE layers by over 50% without sacrificing performance. |
Harder Task Needs More Experts: Dynamic Routing in MoE Models (2024.acl-long)
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Quzhe Huang, Zhenwei An, Nan Zhuang, Mingxu Tao, Chen Zhang, Yang Jin, Kun Xu, Kun Xu, Liwei Chen, Songfang Huang, Yansong Feng
| Challenge: | Unlike existing MoE approaches that rely on fixed TopK Routing, our dynamic expert selection framework dynamically allocates experts based on the confidence level in expert selection for each input. |
| Approach: | They propose a dynamic expert selection framework that dynamically allocates experts based on the confidence level in expert selection for each input. |
| Outcome: | The proposed method achieves an average improvement of 0.7% with less than 90% activated parameters and outperforms dense models in QA and machine translation tasks. |
A Lightweight Mixture-of-Experts Neural Machine Translation Model with Stage-wise Training Strategy (2024.findings-naacl)
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| Challenge: | Using mixture-of-experts (MoE) to deal with language heterogeneity is a challenge in neural machine translation (NMT). |
| Approach: | They propose a lightweight MoE-based NMT model that is trained via an elaborate stage-wise training strategy. |
| Outcome: | The proposed model achieves stable improvements in translation tasks by introducing fewer extra parameters compared to baseline models. |