Unverified50% confidenceFactExact time
Traditional Scaling Laws describe the power-law relationship between model capability and training parameter count, data volume, and compute, with the implicit premise that a model's capability is fixed once it is trained.
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10/16/2026
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UnverifiedThe training-time scaling law was proposed by Kaplan et al. in 2020, holding that model capability is primarily determined by parameter count, training data volume, and training compute.76% similarVerifiedThe Scaling Law proposed by OpenAI reveals that model performance improves in a predictable power-law fashion as the number of parameters, training data volume, and compute increase.75% similarUnverified缩放定律表明模型在特定任务上的表现与训练数据量、模型参数量和计算量之间存在幂律关系，OpenAI、DeepMind等机构的研究支持这一发现75% similarUnverifiedKaplan等人2020年的研究表明模型参数量、训练数据量、训练计算量三者与性能之间存在可预测的幂律关系72% similarUnverifiedFor pure inference scenarios, model weights typically only need to be loaded once, and the constraint of data gravity is far less significant than in the training phase.70% similar
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