Towards Engineering Scaling Laws with Pretraining Data Composition
Researchers demonstrate that neural scaling laws in particle physics can be engineered by optimizing pretraining data composition, shifting computational requirements toward larger datasets rather than bigger models. By using more diverse and task-aligned synthetic data from physics simulators, the study shows improved scaling efficiency for hadronic jet classification, offering a template for other domains with access to high-fidelity generative systems.