Amortizing Scaling Law Construction Costs

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On September 7, 2026, arXiv cs.AI published the paper “Amortizing Scaling Law Construction Costs”. This study addresses the issue of high construction costs associated with large-scale model training due to the huge consumption of data, computing power, and time resources. It proposes a new amortization strategy. The method aims to reduce the marginal cost of model construction per instance by optimizing resource allocation and training processes, thereby enhancing the feasibility and economic efficiency of large-scale model training.

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A arXiv cs.AI en 2026-09-07 12:00

研究人员提出一种高效框架，将数据收集构建为贝叶斯优化问题，旨在大幅降低大规模基础模型缩放定律的构建成本。该框架通过渐进式扩展计算预算来采集数据，并引入代理模拟评估以补充观测配置，从而在无需训练所有配置的情况下恢复完整实验网格。实验发现，这种结合方法能在高达 10 至 100 倍的计算节省下，紧密匹配全密集网格的缩放定律拟合效果。