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Training Communication-Efficient Mixture-of-Experts Language Models with Layer Re-Configuration

Research Efficiency & Systems

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Merged summary

TL;DR - CE-MoE restructures mixture-of-experts language models to use fewer routed expert layers while preserving depth with additional token-mixing and dense feed-forward layers. This reduces costly all-to-all communication without sacrificing model quality.

  • Evaluated across models ranging from 2B to 31.5B total parameters.
  • Matches full-MoE baselines on validation loss and downstream benchmarks under equal total and activated parameter budgets.
  • At 31.5B parameters, CE-MoE requires 33.3% fewer GPU-hours.
  • The largest model also improves average downstream score and inference throughput.

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Training Communication-Efficient Mixture-of-Experts Language Models with Layer Re-Configuration

arXiv cs.AI Simeng Sun, Roger Waleffe 2026-08-28 arXiv:2608.28511
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-25 14:25:51.301148 UTC

TL;DR - CE-MoE restructures mixture-of-experts language models to use fewer routed expert layers while preserving depth with additional token-mixing and dense feed-forward layers. This reduces costly all-to-all communication without sacrificing model quality.

  • Evaluated across models ranging from 2B to 31.5B total parameters.
  • Matches full-MoE baselines on validation loss and downstream benchmarks under equal total and activated parameter budgets.
  • At 31.5B parameters, CE-MoE requires 33.3% fewer GPU-hours.
  • The largest model also improves average downstream score and inference throughput.
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