Research breakthrough · curated entry
Chinchilla scaling laws correct the parameters-to-data ratio
Training Compute-Optimal Large Language Models
- Date
- March 29, 2022
- Category
- Research breakthrough
- Organization / Model
- Google DeepMind
- Evidence
- 1 source · 1 evidence family
DeepMind's Chinchilla showed that under a fixed compute budget, model size and training data should grow in proportion. It corrected the earlier tendency to scale parameters alone.
Curator's note: Cross-checked against the arXiv paper.
Curated sources · 1
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This page is a curated static snapshot and can be cited directly. Preferred format GB/T 7714—2025 (electronic resource [EB/OL]); the other 5 styles open below, together with the machine-readable files.
GB/T 7714—2025 (electronic resource [EB/OL])
AI 纪年. Chinchilla 缩放定律修正参数与数据配比[EB/OL]. (2022-03-29)[2026-11-03]. https://aihist.net/e/chinchilla-2022.html.
APA 7 · American Psychological Association
AI 纪年. (2022, March 29). Chinchilla 缩放定律修正参数与数据配比 [Web page]. https://aihist.net/e/chinchilla-2022.html
MLA 9 · Modern Language Association
"Chinchilla 缩放定律修正参数与数据配比." AI 纪年, 29 March 2022, aihist.net/e/chinchilla-2022.html. Accessed 3 Nov. 2026.
Chicago 17 · notes-bibliography
AI 纪年. "Chinchilla 缩放定律修正参数与数据配比." Published March 29, 2022. Accessed Nov. 3, 2026. https://aihist.net/e/chinchilla-2022.html.
IEEE · citation-reference list
"Chinchilla 缩放定律修正参数与数据配比," AI 纪年, 29 Mar. 2022. [Online]. Available: https://aihist.net/e/chinchilla-2022.html. [Accessed: 3 Nov. 2026].
ACM · author-date variant
AI 纪年. March 29, 2022. Chinchilla 缩放定律修正参数与数据配比. Retrieved November 3, 2026 from https://aihist.net/e/chinchilla-2022.html.
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