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DeepMind'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 parameters
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9/11/2026
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VerifiedDeepMind's Chinchilla research (2022) pointed out that optimal training should keep data volume and parameter count growing at roughly a 1:1 ratio77% 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.76% similarUnverified根据Chinchilla定律及其后续研究，模型性能与训练计算量之间存在幂律关系75% similarUnverifiedDeepMind's Chinchilla research pointed out that previous models were generally oversized and undertrained, meaning they had too many parameters but insufficient training data.74% 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% similar
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