Unverified70% confidenceFactExact time
The paper "Scaling Laws for Neural Language Models" by Kaplan et al. came from OpenAI and was the first to systematically describe the relationship between model performance and parameter count, data size, and compute using power laws.
2
Sources
70%
Confidence
Long-term
Relevance
8/19/2026
First Seen
Sources
Rethinking Scaling Laws: Parameters Are Not the Only Answer
twitterjietang8/19/2026
Related Entities
Related Claims
VerifiedThe 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 compute74% similarUnverified推理时计算（Test-Time Compute）的理论基础可追溯至2020年OpenAI发布的《Scaling Laws for Neural Language Models》论文74% similarUnverified扩展假设由OpenAI研究员Jared Kaplan等人于2020年的扩展定律论文中量化阐述，核心是模型性能与参数量、数据量、计算量存在幂律关系74% similarVerifiedIn 2020, Kaplan et al. at OpenAI revealed that model performance follows predictable power-law relationships with parameter count, training data volume, and compute budget.72% similarVerifiedThe Scaling Law was first systematically quantified by Kaplan et al. in 2020 in language model research, pointing out a power-law relationship between model performance and parameter count, data volume, and compute.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/796923API
curl https://kongchang.com/api/v1/knowledge/claims/796923MCP
get_claim(id=796923)