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News/RAZOR: Pruning Replaceable Experts in LLMs
arxiv
PublishedOctober 2, 2026 at 4:00 AM
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RAZOR: Pruning Replaceable Experts in LLMs

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Publisher summary· verbatim

arXiv:2609.30465v4 Announce Type: replace-cross Abstract: Mixture-of-experts (MoE) models activate only a few experts per token but store the entire expert pool. Pruning this pool requires identifying experts whose removal preserves model behavior. Routing frequency and output magnitude do not fully

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