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% similarUnverifiedThe current mainstream paradigm centers on the Scaling Law—simultaneously expanding parameters, data and compute yields predictable performance gains.78% 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% similarVerifiedInference-time scaling improves model performance by increasing computation during the inference phase, and exhibits a power-law relationship similar to that of pre-training.72% similar
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