Verified80% confidenceFactTime unknown
The Chinchilla law states that under a fixed compute budget, appropriately reducing model size and increasing training data volume often yields better performance.
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6/1/2026
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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.82% similarUnverifiedThe paper found that for a fixed compute budget, training a smaller model for longer tends to yield better results than training a larger model, which contradicts the Chinchilla law.79% similarUnverifiedThe Chinchilla scaling law states that the optimal number of training tokens should be proportional to the number of model parameters, meaning that when parameters are doubled, training data and computational cost should at least double as well.78% similarUnverifiedChinchilla论文修正了预训练中计算与数据的最优比例关系75% similarVerifiedDeepMind's Chinchilla research (2022) pointed out that optimal training should keep data volume and parameter count growing at roughly a 1:1 ratio74% similar
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