Union-of-Experts: Neurons in Mixture-of-Experts are Secretly Routers (2026.acl-long)
Copied to clipboard
| Challenge: | Mixture-of-Experts (MoE) models rely on an external router to assign tokens to experts, resulting in suboptimal performance. |
| Approach: | They propose an MoE variant that performs "expert-autonomous routing" by pre-designating a fraction of neurons within each expert as "routing neurons" they pre-train UoE models with up to 3B parameters and show they outperform traditional MoEs with matched efficiency. |
| Outcome: | The proposed model outperforms existing models with 3B parameters and provides valuable insights into expert-autonomous selection and the broader routing mechanisms of MoE models. |
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 . |
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. |
On the Benefits of Learning to Route in Mixture-of-Experts Models (2023.emnlp-main)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Infinity-MoE: Generalizing Mixture of Experts to Infinite Experts (2026.eacl-short)
Copied to clipboard
| Challenge: | Existing methods to increase the number of experts are -MoE and . |
| Approach: | They propose a mixture of experts that selects a few feed-forward networks per token to increase the number of experts. |
| Outcome: | The proposed model improves on a GPT-2 Small model with 129M active and 186M total parameters by 2.5% over the current model. |
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 . |
Maximum Score Routing For Mixture-of-Experts (2025.findings-acl)
Copied to clipboard
| Challenge: | Traditional mixture-of-experts (MoE) networks impose an expert capacity constraint to ensure GPU-friendly computation. |
| Approach: | They propose a routing paradigm that dynamically allocates input tokens to top-k experts through differentiable sparse transformations, enabling scalable model capacity while preserving computational efficiency. |
| Outcome: | The proposed model achieves lower training losses and higher evaluation scores at equivalent FLOPs compared to constrained and unconstrained baselines. |
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation (2023.findings-acl)
Copied to clipboard
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. |
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. |
Part-Of-Speech Sensitivity of Routers in Mixture of Experts Models (2025.coling-main)
Copied to clipboard
| Challenge: | a study examines the behavior of routers in Mixture of Experts (MoE) models . experts with similar linguistic traits are often routed to the same expert regardless of context . |
| Approach: | They investigate how tokens are routed based on their linguistic features . they aim to explore whether experts specialize in processing tokens with similar linguistic traits . |
| Outcome: | The proposed model-integrated routers are based on Mixture of Experts (MoE) models . the results show that expert specialization is high for POS categories . |