arXiv Machine Learning
6d ago

Predictable Scaling: Efficient Model Exploration via Scaling-Law Prediction

Predictable Scaling (PS) is a framework that uses small‑scale training proxies to forecast full‑scale model performance, reducing the need for expensive GPU‑hourly runs. By applying composable scaling functions and curve fitting with uncertainty estimates, PS can rank candidate changes accurately across different hypothesis types. Experiments on two ranking models show PS achieves 79–88% compute savings while maintaining correct ranking of actionable hypotheses.

By Aishwarya Wesanekar, Sheng-Tao Yang, Jiawei Zhang, Flavio Sales Truzzi, Chen Chen, Nader Beigiparast, Shengyuan Huang, Roshanak Zilouchian Moghaddam, Vitor Cid, Colin Clement, Vinayak Thapliyal, Sebastian Ament, Jihao Andreas Lin, Vincent Huang, Kai Han, Boyi Jiang, Wenlin Chen, Chaorong Chen, Ashwin Kumar, Maximilian Balandat, David Eriksson, Yifei Zhang, Pan Zhong