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cs.LG2026
Scaling with Collapse: Efficient and Predictable Training of LLM Families
Shane Bergsma, Bin Claire Zhang, Nolan Dey +3
Effective LLM training depends on predictable scaling of key quantities -- such as final loss and optimal hyperparameters -- with model and dataset size. Qiu et al. (2025) recently…
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…