Structure Aware Neural Architecture Search for Mixture of Experts

arXiv:2608.29817 · cs.LG · Submitted 2026-08-30 · Read on arXiv

cs.LG

Submitted: 2026-08-30

Updated: 2026-08-30

Code: https://github.com/intsystems/nas-for-moe

Terminology

• Benchmark — 14× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Neural Architecture Search — 13× in this paper
• Mixture of Experts — 11× in this paper · explained in 2 episodes
• Clustering — 8× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Mixture-of-Experts (MoE) — 4× in this paper · explained in 7 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Loss Function — 2× in this paper · explained in 2 episodes
• Time Series Forecasting — 2× in this paper · explained in 3 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Mixture-of-Experts (MoE) Models — 1× in this paper · explained in 3 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications
• Mixtral of Experts
• DARTS: Differentiable Architecture Search
• Mixture of experts models for multilevel data: modelling framework and approximation theory
• Multi-Source Domain Adaptation with Mixture of Experts
• A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts
• Divide, Specialize, and Route: A New Approach to Efficient Ensemble Learning
• Learning Factored Representations in a Deep Mixture of Experts
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
• Scaling Vision with Sparse Mixture of Experts
• DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
• Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models
• Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models
• Scaling Expert Language Models with Unsupervised Domain Discovery
• Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
• OLMoE: Open Mixture-of-Experts Language Models
• Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
• Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
• Neural Architecture Search with Reinforcement Learning
• Designing Neural Network Architectures using Reinforcement Learning

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