Unverified50% confidenceFactExact time
The Chinchilla optimal scaling strategy proposed by DeepMind states that for a given compute budget, there is an optimal ratio of parameter count to data volume, approximately 1:20.
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50%
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Long-term
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8/6/2026
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Related Claims
UnverifiedDeepMind于2022年的Chinchilla论文修正了Scaling比例，发现相同计算预算下应使用更小模型但喂入更多数据78% similarUnverified根据DeepMind在2022年提出的Chinchilla缩放定律，一个700亿参数的模型需要约1.4万亿token才能达到计算最优73% similarUnverified参数量在1亿以下的轻量模型经领域数据微调后可达95%以上意图分类准确率，单次推理延迟控制在5-20ms以内68% similarUnverified2022年Hoffman等人的Chinchilla论文重新校准了算力最优的参数-数据比例，指出此前许多大模型训练不充分65% similarUnverifiedAssuming the data follows a Gaussian distribution, maximizing the likelihood is equivalent to minimizing the mean squared error.64% similar
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