2 papers
cs.LG2026
Don't be lazy: CompleteP enables compute-efficient deep transformers
Nolan Dey, Bin Claire Zhang, Lorenzo Noci +6
We study compute efficiency of LLM training when using different parameterizations, i.e., rules for adjusting model and optimizer hyperparameters (HPs) as model size changes. Some…
cs.LG2025
Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer
Yihe Dong, Lorenzo Noci, Mikhail Khodak +1
The transformer architecture is central to the success of modern Large Language Models (LLMs), in part due to its surprising ability to perform a wide range of tasks - including ma…