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Particle physics models engineered for data-driven scaling laws

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员正在探索如何为粒子物理学模型设计工程化缩放定律，并将其与大型语言模型进行类比。与自然语言或图像领域不同，基础物理学受益于高保真模拟器，这些模拟器能够经济高效地生成合成数据。这使得数据集工程能够影响模型缩放，倾向于更多数据而非更大的参数。对于区分强子射流的任务，一项研究表明，通过包含多样化且与任务对齐的预训练数据，可以改变缩放行为，使其需要更多数据。 AI

影响 这项研究可能通过优化数据构成，从而实现科学领域更高效的模型训练。

排序理由 关于粒子物理学中缩放定律的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Particle physics models engineered for data-driven scaling laws

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关于粒子物理学中缩放定律的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jan-Lucas Uslu, Kevin Greif, Daniel Whiteson, Benjamin Nachman ·

    迈向预训练数据组成工程缩放定律

    arXiv:2606.19781v1 Announce Type: cross Abstract: Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size. While well-established for large language models, these relationships are emerging for large models in particle p…

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