Unverified50% confidenceFactExact time
Chinchilla研究发现在固定计算预算下参数量和训练token数应按大致1:20比例匹配
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8/27/2026
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Unverified2022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73% similarUnverifiedThe 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 1:20.73% similarVerifiedIn 2022, Hoffmann et al.'s Chinchilla paper redid experiments on four hundred models and concluded that, under the premise of compute-optimality, each parameter should correspond to approximately 20 tokens.66% similarUnverifiedChinchilla定律大致按每个参数分配20倍token的比例来安排算力65% similarUnverifiedThe research team measured inference and training speed improvements of more than 20% on a billion-parameter-scale sparse LLM.64% similar
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