arXiv:2605.28940v1 Announce Type: cross Abstract: Recently observed empirical scaling laws describe the performance of foundation-type models as three independent key quantities -- dataset size, compute, and model parameters -- are modified. Extracting these scaling laws informs the training of large complex models for which the tuning of hyperparameters in traditional ways is not feasible. This work for the first time explores if scaling laws can also be observed for the task of particle jet generation -- both relevant as a pre-training objective for foundation models and as in-situ simulatio
The accelerating pace of AI development necessitates more efficient model training, making the extraction of scaling laws critical for managing large and complex AI systems.
Understanding scaling laws enhances the ability to train large AI models more effectively, reducing computational waste and accelerating scientific discovery, particularly in fields like high-energy physics.
The methodology for optimizing large AI models and potentially simulations, leading to more predictable performance gains and resource allocation.
- · AI research institutions
- · Particle physics community
- · Cloud computing providers
- · Foundation model developers
- · Organizations using inefficient AI training methods
Improved efficiency and performance in training large foundation models.
Faster development and deployment of advanced AI applications across various scientific and industrial domains.
Reduced resource expenditure for achieving state-of-the-art AI capabilities, potentially democratizing access to large-scale AI for certain applications.
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