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About this paper
Alexander Stern, Tim Rocktäschel, Acyr Locatelli, Laura Ruis
The authors test whether synthetic pre-pretraining (PPT) is a better use of a fixed training-compute budget than natural-language training. They compare synthetic-first and natural-language-only training across scarce-data, plentiful-data, and high-token regimes. Across these tested compute-matched settings, synthetic PPT does not outperform natural-language training on language modeling; this finding is limited to the tested recipes and evaluations.
Summary by alphaXiv.
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