Data curation is an essential component of large-scale pretraining.
In this work, we demonstrate that jointly
selectingbatchesof data is more effective for learning than selecting examples independently. Multimodal contrastive objectives expose the dependencies between data and thus naturally yield criteria for measuring thejoint learnabilityof a batch. We derive a simple and tractable algorithm for selecting such batches, which significantly accelerate training beyond individually-prioritized data points.
As performance improves by selecting from larger super-batches, we also leverage recent advances in model approximation to reduce the associated computational overhead. As a result, our approach—multimodal contrastive learning with joint example selection (JEST) —surpasses state-of-the-art models with up to 13fewer iterations and 10less computation. Essential to the performance of JEST is the ability to steer the data selection process towards the distribution of smaller, well-curated datasets via pretrained reference models, exposing the level of data curation as a new dimension for neural scaling laws.