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Cluster-driven expert pruning for mixture-of-experts large language models.arXiv preprint arXiv:2504.07807, 2025

Hongcheng Guo, Juntao Yao, Boyang Wang, Junjia Du, Shaosheng Cao, Donglin Di, Shun Zhang, Zhoujun Li · 2025 · arXiv 2504.07807

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dMoE: dLLMs with Learnable Block Experts

cs.CL · 2026-05-29 · unverdicted · novelty 6.0

dMoE aggregates token expert distributions to block level in dLLMs, cutting unique experts from 69.5 to 14.6, memory by 76-80%, and latency by 1.14-1.66x while retaining 99.11% performance.

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  • dMoE: dLLMs with Learnable Block Experts cs.CL · 2026-05-29 · unverdicted · none · ref 20

    dMoE aggregates token expert distributions to block level in dLLMs, cutting unique experts from 69.5 to 14.6, memory by 76-80%, and latency by 1.14-1.66x while retaining 99.11% performance.