Use active learning to strategically pick which small experiments to run when fitting scaling laws—you can predict large-scale model performance with 90% less compute by choosing experiments that reduce uncertainty about the target region you care about.
Training large AI models costs millions, and figuring out how they'll scale costs millions more. This paper proposes a smarter way to choose which smaller pilot experiments to run so you can accurately predict how a massive training run will perform, using only about 10% of the budget that naive approaches would need.