Verified80% confidenceFactExact time
DeepMind's Chinchilla research (2022) pointed out that optimal training should keep data volume and parameter count growing at roughly a 1:1 ratio
8
Sources
80%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
Related Claims
VerifiedIn 2022, DeepMind's Chinchilla paper pointed out that under the same compute budget, model size and training data volume should be scaled up proportionally.83% similarUnverifiedDeepMind's Chinchilla scaling law (from the 2022 paper "Training Compute-Optimal Large Language Models") shows that model performance depends on the optimal ratio of parameter count to training token count, with the optimal training volume being approximately 20 times the number of parameters77% similarUnverifiedChinchilla论文修正了预训练中计算与数据的最优比例关系77% similarUnverifiedDeepMind's Chinchilla research pointed out that previous models were generally oversized and undertrained, meaning they had too many parameters but insufficient training data.77% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/489536API
curl https://kongchang.com/api/v1/knowledge/claims/489536MCP
get_claim(id=489536)