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Unverified50% confidenceFactExact time

2022年DeepMind的Chinchilla论文指出同等算力预算下应均衡增大模型尺寸与训练数据量

1

Sources

50%

Confidence

Long-term

Relevance

7/16/2026

First Seen

Sources

微软印度研究院：AI时代从软件到基础设施公司的转型之路

Related Claims

VerifiedDeepMind的Chinchilla研究（2022）指出最优训练应让数据量与参数量保持大致1:1的比例增长83% similar UnverifiedChinchilla定律指出在固定计算预算下，适当缩小模型规模并增加训练数据量往往能获得更好的性能82% similar UnverifiedChinchilla缩放定律指出最优训练token数应与模型参数量成正比，参数翻倍时训练数据和计算成本至少翻倍73% similar Unverified论文发现对于固定的算力预算，训练一个更小的模型训练更长时间，往往比训练大模型效果更好，这与Chinchilla定律相悖73% similar Unverified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70% similar

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