Data Quality is the Compute Multiplier

Better data quality is the highest-leverage and most underinvested part of building a model: it produces a better model for the same compute, whether you're mid-training on an open base or pre-training from scratch.
This session is a practical look at data curation, covering what data quality actually means, the stages of a modern curation pipeline (cleaning, filtering, deduplication, synthetic data generation, algorithmic mixing, and multi-stage composition), and which steps matter most in practice. It draws on DatologyAI's frontier data research and customer results, including Thomson Reuters' mid-training gains on proprietary legal domain data and Arcee's Trinity model reaching the open frontier on public data alone. You'll leave with a concrete sense of where better data quality pays off and how data curation is shaping the future of model training.