2 papers
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…
cs.LG2025
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…