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https://arxiv.org/pdf/2202.09368v1
Abstract Sparsely-activated Mixture - of-experts (MoE) models allow the number of parameters to greatly increase while keeping the amount of computation for a given token or a given sample unchanged. However, a poor expert routing strategy (e.g. one resulting in load imbalance) can cause certain experts to be under-trained, leading to an expert being under or over-specialized. Prior work ...
1 day ago ·
https://que.com/how-mixture-of-experts-architecture-is-reshaping-machine-learning-efficiency/
1 day ago · This expert -autonomous routing aligns routing decisions with each expert ’s functional profile, leading to improved parameter efficiency in pre-training experiments at the 3-billion parameter scale.
Nov 16, 2022 ·
https://research.google/blog/mixture-of-experts-with-expert-choice-routing/
Nov 16, 2022 · Compared to token-based routing and other routing methods in traditional MoE networks, EC demonstrates very strong training efficiency and downstream task scores. Our method resonates with one of the vision for Pathways, which is to enable heterogeneous mixture - of-experts via Pathways MPMD (multi program, multi data) support.
Nov 22, 2025 ·
https://mljourney.com/mixture-of-experts-moe-routing-algorithms-for-sparse-llms/
Nov 22, 2025 · Mixture - of-Experts (MoE) routing algorithms for sparse LLMs offer an elegant solution to this challenge, enabling models to achieve the capacity of dense networks while activating only a fraction of parameters for each input. Understanding these routing algorithms is crucial for anyone working with or developing modern efficient language models.
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