Hoffmann et al. (2022) propose three methods for estimating a compute-optimal scaling law. We attempt to replicate their third estimation procedure, which involves fitting a parametric loss function to a reconstruction of data from their plots. We find that the reported estimates are inconsistent with their first two estimation methods, fail at fitting the extracted data, and report implausibly narrow confidence intervals--intervals this narrow would require over 600,000 experiments, while they likely only ran fewer than 500. In contrast, our rederivation of the scaling law using the third approach yields results that are compatible with the findings from the first two estimation procedures described by Hoffmann et al.

翻译：Hoffmann 等人（2022）提出了三种估算计算最优缩放定律的方法。我们尝试复制其第三种估算过程，该方法涉及从图中数据重建结果来拟合参数化损失函数。我们发现其报告的估计值与前两种估算方法不一致，无法有效拟合提取数据，且给出了不切实际的窄置信区间——如此窄的区间需要超过60万次实验，而他们可能仅进行了不到500次实验。相比之下，我们使用第三种方法重新推导的缩放定律所得结果，与Hoffmann等人所述的前两种估算程序的结果一致。