Cerebras: Layer Dropout Speeds Up LLM Training and Enables Early-Exit Inference
cerebras · hf · 2026-09-07
Cerebras argues against dropping dropout: optimizing layer sparsity via layer dropout improves LLM training efficiency and unlocks faster inference.
Models trained with the technique support early exit for easy inputs and speculative decoding acceleration, all without sacrificing accuracy—turning a regularization trick into an inference speedup.
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