1 citations · 1 across the 7 of their papers we have counts for
4 papers · 1 filter
Small-Scale Experiments: Are We There Yet?
Nicholas Lourie, Kyunghyun Cho, Karen Ullrich +1
Scaling laws promised cost-effective experiments; six years later, they have yet to fully deliver. Instead, researchers have found them unreliable at small scales (starting at 4M p…
Neural Neural Scaling Laws
Michael Y. Hu, Jane Pan, Ayush Rajesh Jhaveri +2
Neural scaling laws predict how language model performance improves with increased training inputs. While aggregate metrics like validation loss can follow smooth power-law curves,…
Hyperparameter Loss Surfaces Are Simple Near their Optima
Nicholas Lourie, He He, Kyunghyun Cho
Hyperparameters greatly impact models' capabilities; however, modern models are too large for extensive search. Instead, researchers design recipes that train well across scales ba…
Aioli: A Unified Optimization Framework for Language Model Data Mixing
Mayee F. Chen, Michael Y. Hu, Nicholas Lourie +2
Language model performance depends on identifying the optimal mixture of data groups to train on (e.g., law, code, math). Prior work has proposed a diverse set of methods to effici…