arXiv:2601.02144v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) architectures scale large language models efficiently by employing a parametric ``router'' to dispatch tokens to a sparse subset of experts. Typically, this router is trained once and then frozen, rendering routing decisions brittle under distribution shifts. We address this limitation by introducing kNN-MoE, a retrieval-augmented routing framework that reuses locally optimal expert assignments from a memory of similar past cases. This memory is constructed offline by directly optimizing token-wise routing logits to m
The increasing scale and complexity of large language models are pushing the boundaries of efficient architecture design, making dynamic routing mechanisms crucial for continued performance gains.
This development allows large language models to adapt more effectively to new data distributions, improving their robustness and reducing the need for constant, costly retraining of routing components.
MoE architectures can now maintain more optimal expert assignments over time, moving beyond brittle frozen routers and leading to more adaptable and efficient AI models.
- · AI researchers and developers
- · Companies deploying large language models
- · Users of advanced AI applications
- · Fixed-architecture AI solutions
- · Legacy AI model optimization techniques
Improved efficiency and adaptability of large language models (LLMs) in MoE architectures.
Reduced operational costs for LLMs due to fewer retraining cycles and better performance on shifted data.
Accelerated development of more complex and specialized AI models, potentially leading to new AI applications and services.
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