English(EN) Predict before you train: Scaling Laws for particle physics foundation models

AI规模定律在训练前预测粒子物理模型性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-31 04:00

研究人员开发了一种方法，利用规模定律在粒子物理领域的大型机器学习模型训练完成之前预测其性能。通过在较小的模型上拟合联合模型和数据规模定律，他们能够以高精度预测使用更多计算资源训练的显著更大的模型的损失。这种预测能力可以将计算预算转化为预期的物理性能，有助于为诸如射流标记和背景拒绝等任务有效分配资源。 AI

影响能够更有效地分配计算资源，用于在科学领域训练大型AI模型。

排序理由该条目是arXiv预印本，详细介绍了应用于粒子物理的机器学习规模定律的新研究方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jan-Lucas Uslu, Benjamin Nachman, Christopher Re · 2026-07-31 04:00

训练前预测：粒子物理基础模型的规模定律

arXiv:2607.23377v1 Announce Type: cross Abstract: 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, bu…

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报道来源 [1]

训练前预测：粒子物理基础模型的规模定律

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