Predict before you train: Scaling Laws for particle physics foundation models
The largest machine learning models in particle physics are also the most expensive to train, yet the return on scaling a given architecture cannot be estimated before that compute is spent. Scaling laws have been fit for jets, but none has yet been shown to predict the performance of models it was not fit on. We show that, for a generic transformer pretrained on collider jets, it can be forecast. Fitting a joint model-and-data scaling law on small models alone, spanning three orders of magnitude of training compute, we predict the loss of models trained afterward with more than one hundred times more compute to within one percent. We then connect the forecast to downstream physics perfor...
| Authors | Jan-Lucas Uslu, Benjamin Nachman, Christopher Re |
|---|---|
| Primary category | hep-ex |
| Published | 25 July 2026 |
| Last updated at source | 25 August 2026 |
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