Data Quality is the Compute Multiplier

Official Schedule Context

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.

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