איכות הנתונים היא כפולת החישוב

תקציר מקורי באנגליתSwap compute for data on the scaling curve and the same money buys a better model, which is why Ari Morcos calls data quality the compute multiplier and the most underinvested part of training. His frame is an oil refinery for data rather than a firehose: clean, curate, create, and compose, with quality classifiers, deduplication, and synthetic generation each earning their place, and the sequencing across stages mattering as much as any single step. The scarce resource now is not tokens but signal per token, and finding data that is optimal for a given target is where the leverage hides. The proof points are concrete. Better curated data lets a small multilingual model beat far larger ones trained on many more tokens, and it buys real inference efficiency because a model reaches the same