How to scale your HEP ML models: A recipe for robust architecture comparisons at scale

Paper recorded by Signals 4 on 2026-10-05 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-05 on arXiv · recorded by Signals 4 on 2026-10-06

Category: cs.LG · 机器学习 · first seen 2026-10-06

Abstract

Much of the recent progress in machine learning domains such as language models has come from scaling laws that predict performance as a function of training effort. In high-energy physics (HEP) similar behavior has now been observed. To aid further study, we present a systematic procedure to derive robust scaling laws and compare design choices on the relevant budget axes for HEP tasks. We first

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