Verified80% confidenceFactExact time
Scaling Law refers to the predictable and regular improvement in model capability as the number of parameters and the volume of training data increase.
6
Sources
80%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
Related Claims
VerifiedScaling Law was discovered by OpenAI researchers in 2020 and states that model performance improves predictably when model parameter count, training data volume, and compute scale at specific ratios.81% similarUnverified规模扩展（Scaling Law）带来涌现能力，即模型在参数突破某一阈值后突然习得此前未经专门训练的推理、类比与创作能力77% similarVerifiedScaling Law research has found that model performance exhibits a smooth power-law relationship with the number of model parameters N, dataset size D, and training compute C74% similarVerified推理时扩展（inference-time scaling）通过在推理阶段增加计算量提升模型性能，存在类似预训练的幂律关系72% similarUnverifiedThe core finding of the Chinchilla scaling law is that under a fixed compute budget, simultaneously increasing model parameters and training data volume is required to achieve optimal performance.71% similar
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