  Vision-language models (VLMs) are trained for thousands of GPU hours on
carefully curated web datasets. In recent times, data curation has gained
prominence with several works developing strategies to retain 'high-quality'
subsets of 'raw' scraped data. For instance, the LAION public dataset retained
only 10% of the total crawled data. However, these strategies are typically
developed agnostic of the available compute for training. In this paper, we
first demonstrate that making filtering decisions independent of training
compute is often suboptimal: the limited high-quality data rapidly loses its
utility when repeated, eventually requiring the inclusion of 'unseen' but
'lower-quality' data. To address this quality-quantity tradeoff
($\texttt{QQT}$), we introduce neural scaling laws that account for the
non-homogeneous nature of web data, an angle ignored in existing literature.
Our scaling laws (i) characterize the $\textit{differing}$ 'utility' of various
quality subsets of web data; (ii) account for how utility diminishes for a data
point at its 'nth' repetition; and (iii) formulate the mutual interaction of
various data pools when combined, enabling the estimation of model performance
on a combination of multiple data pools without ever jointly training on them.
Our key message is that data curation $\textit{cannot}$ be agnostic of the
total compute that a model will be trained for. Our scaling laws allow us to
curate the best possible pool for achieving top performance on Datacomp at
various compute budgets, carving out a pareto-frontier for data curation. Code
is available at https://github.com/locuslab/scaling_laws_data_filtering.
