arXiv:2605.19407v1 Announce Type: cross
Abstract: We investigate data filtering for large model pretraining via new scaling studies that target the high compute, data-scarce regime. In spite of an apparently common belief that filtering data to include only high-quality information is essential, our experiments suggest that with enough compute, the best data filter is no data filter. We find that sufficiently trained large parameter models not only tolerate low-quality and distractor data, but in fact benefit from nominally “poor” data.
Feasibility testing of a home-based exercise intervention in children with cerebral palsy who are ambulant—a study protocol of the HOME-EX study
Children gain increased health and well-being by participating in physical activity. Children with cerebral palsy who are ambulatory (CP-A) are known to be less physically