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Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference

Research Efficiency & Systems

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TL;DR - A large-scale study finds that properly optimized layer dropout can reduce both LLM training compute and inference costs without sacrificing accuracy. Across more than 2,400 experiments, it saved up to 25% of training FLOPs and enabled up to 1.5× faster inference.

  • Effective layer dropout requires optimizing its layer distribution, schedule, and optimizer hyperparameters.
  • At equal training FLOPs, layer dropout produced lower loss; at equal training steps, it achieved similar or lower validation loss with less compute.
  • Dropout-trained models supported early exit, intermediate-layer skipping, and self-speculative decoding with negligible accuracy loss.
  • Results covered 271M–8.2B-parameter models and datasets up to 160B tokens, trained on Cerebras CS-3 systems.

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Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference

arXiv cs.AI Mostafa Elhoushi, Alex Pretko, Nolan Dey, Bin Claire Zhang, Gavia Gray, Gurpreet Gosal, Abdulrahman Mahmoud, Shane Bergsma, Joel Hestness 2026-09-04 arXiv:2609.05275
Public signals Hugging Face upvotes 25 · Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · Upvotes 25 OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-25 14:23:33.729233 UTC

TL;DR - A large-scale study finds that properly optimized layer dropout can reduce both LLM training compute and inference costs without sacrificing accuracy. Across more than 2,400 experiments, it saved up to 25% of training FLOPs and enabled up to 1.5× faster inference.

  • Effective layer dropout requires optimizing its layer distribution, schedule, and optimizer hyperparameters.
  • At equal training FLOPs, layer dropout produced lower loss; at equal training steps, it achieved similar or lower validation loss with less compute.
  • Dropout-trained models supported early exit, intermediate-layer skipping, and self-speculative decoding with negligible accuracy loss.
  • Results covered 271M–8.2B-parameter models and datasets up to 160B tokens, trained on Cerebras CS-3 systems.
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