551 citations · 2.1k across the 36 of their papers we have counts for
12 papers · 1 filter
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Avi Singh, John D. Co-Reyes, Rishabh Agarwal +38
Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of hi…
Small-scale proxies for large-scale Transformer training instabilities
Mitchell Wortsman, Peter J. Liu, Lechao Xiao +13
Teams that have trained large Transformer-based models have reported training instabilities at large scale that did not appear when training with the same hyperparameters at smalle…
Second-order regression models exhibit progressive sharpening to the edge of stability
Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington
Recent studies of gradient descent with large step sizes have shown that there is often a regime with an initial increase in the largest eigenvalue of the loss Hessian (progressive…
Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm
Lechao Xiao, Jeffrey Pennington
Although learning in high dimensions is commonly believed to suffer from the curse of dimensionality, modern machine learning methods often exhibit an astonishing power to tackle a…
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression
Lechao Xiao, Hong Hu, Theodor Misiakiewicz +2
As modern machine learning models continue to advance the computational frontier, it has become increasingly important to develop precise estimates for expected performance improve…
Temperature check: theory and practice for training models with softmax-cross-entropy losses
Atish Agarwala, Jeffrey Pennington, Yann Dauphin +1
The softmax function combined with a cross-entropy loss is a principled approach to modeling probability distributions that has become ubiquitous in deep learning. The softmax func…