Unverified60% confidenceFactExact time
Scaling Law research found that model performance (measured by cross-entropy loss) has a predictable power-law relationship with model parameter count, dataset size, and computational cost.
2
Sources
60%
Confidence
Long-term
Relevance
8/28/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedScaling 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 C80% similarVerifiedThe scaling laws were systematically demonstrated in 2020 by OpenAI researcher Jared Kaplan and others, with the core finding that the cross-entropy loss of language models follows a power-law relationship with model parameter count, training data volume, and compute76% similarUnverifiedScaling laws were initially widely validated in language model research, referring to the predictable power-law relationship of model performance as model scale, data volume, and compute increase.74% similarUnverifiedScaling laws reveal a near power-law relationship between model loss and the number of parameters, data volume, and compute74% similarUnverifiedScaling Laws refer to the power-law relationship between model performance (such as loss function values) and model parameter count, data volume, and compute.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/815971API
curl https://kongchang.com/api/v1/knowledge/claims/815971MCP
get_claim(id=815971)