Data Quality Is the Compute Multiplier — Ari Morcos, DatologyAI
In an era of increasing compute scarcity—evidenced by rising H100 prices and skyrocketing token usage—data quality has emerged as the critical 'compute multiplier' for model training. The presentation outlines a systematic approach to data enhancement through four stages: Clean, Curate, Create, and Compose. By maximizing the signal per token (marginal information gain), organizations can achieve performance levels comparable to models trained with vastly more compute budgets. Practical applications include improving Vision Language Models (VLMs) and enhancing multilingual capabilities using proprietary or public datasets.
Key takeaways
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Compute Scarcity Drives Data Focus
The availability of compute is becoming increasingly constrained, leading to market actions like Google capping Meta's Gemini usage and OpenAI selling token futures. This necessitates a shift in focus from raw compute power to data quality.
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Data Quality as Compute Multiplier
3:39
Improving data quality allows for dramatically better performance (blue curve) compared to training with the same limited compute budget (gray curve), effectively simulating much larger compute investments.
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The Four C's of Data Enhancement
5:48
Data improvement is achieved through a pipeline: Clean (heuristic filters, decontamination), Curate (quality classifiers, redundancy reduction), Create (synthetic data generation/rephrasing), and Compose (sequencing across multiple training stages).
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Cross-Lingual Benefits from Curation
15:24
Curating English data can positively benefit non-English performance, demonstrating cross-lingual transfer. Similarly, curating non-English data benefits English performance.
