Approximating Pretrain Quality via Single-Forward Pass
RyanGreenblatt · x · 2026-07-17
Ryan Greenblatt initially noted he had linked the wrong thing earlier, then clarified: he ran a quick experiment using a specific dataset, adopting **performance on math problems in a single forward pass** as an approximate metric. He explained that while this metric isn't perfect, it generally correlates with pretrain quality and, importantly, cannot be improved by post-training. In contrast, loss might be a better indicator, but because the base models of closed-weight models are inaccessible, loss cannot be measured directly.
Related event: Evaluating Pretrain Quality via Math Zero-Shot Performance(2 posts)→
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