Verified85% confidenceFactExact time
In 2022, DeepMind's Chinchilla paper pointed out that under the same compute budget, model size and training data volume should be scaled up proportionally.
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85%
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Long-term
Relevance
7/16/2026
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Related Claims
VerifiedDeepMind's Chinchilla research (2022) pointed out that optimal training should keep data volume and parameter count growing at roughly a 1:1 ratio83% similarVerifiedThe Chinchilla law states that under a fixed compute budget, appropriately reducing model size and increasing training data volume often yields better performance.82% 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.73% 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.73% 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 parameters73% similar
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