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Data Quality is the Compute Multiplier

Conference Context

Session Description

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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Notes

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Synthesized Breakdown

Data Quality is the Compute Multiplier ## Conference Context - Date/time: 2026-06-29 · 10:45am-11:05am - Track/room: Data Quality · Track 9 - Speaker(s): Ari Morcos - Session type/status: session · confirmed - Track: Data Quality - Room: Track 9 - Session type: session - Status: confirmed ## Session Description 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.

Speaker And Company Context

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Derived Links And Source Material

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Evidence Boundary

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