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News/Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference
arxiv
PublishedSeptember 7, 2026 at 4:00 AM
—neutral

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

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arXiv:2609.05275v1 Announce Type: new Abstract: Layer dropout (a.k.a. stochastic depth) has been shown to enable faster training, higher accuracy, and robustness to zero-shot layer pruning in both language and vision transformers. However, as models and datasets have scaled, dropout - particularly l

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