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cs.AI2026
Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference
Mostafa Elhoushi, Alex Pretko, Nolan Dey +6
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 transformer…
cs.LG2026
Unlocking Lossless Speedups in LLMs via Discrete Diffusion
Subham Sekhar Sahoo, Lingjie Chen, Khiem Pham +14
Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation. To overco…