arXiv:2606.07597v1 Announce Type: new Abstract: Pre-training data mixtures are commonly tuned by running small-scale experiments and extrapolating to the target training budget. When high-quality data is scarce and must be repeated, this extrapolation frequently fails, but the source of the failure has not been isolated. We show that a primary culprit is a repetition mismatch: because high-quality datasets are small, their repetition rate changes as the training budget grows, shifting the optimal mixture in ways that small-scale proxy experiments do not anticipate. A subsampling procedure that
The increasing scale and complexity of AI models, especially with limited access to truly novel, high-quality data, necessitate robust data mixture optimization techniques.
Efficient and accurate data mixture strategies are crucial for optimizing AI training budgets, particularly as compute costs escalate and data scarcity becomes a bottleneck.
The understanding of how data repetition impacts AI training will lead to improved experimental methodologies and more effective scaling of pre-training mixtures.
- · AI compute infrastructure providers
- · Organizations with proprietary high-quality datasets
- · AI research labs focused on data efficiency
- · AI projects relying on suboptimal data mixture heuristics
- · Organizations without sufficient data engineering expertise
More efficient allocation of compute resources for large-scale AI model training will occur.
Improved data mixture optimization could accelerate the development of more capable and cost-effective AI models.
This could contribute to a competitive advantage for entities that master data efficiency, potentially exacerbating the divide between AI leaders and laggards.
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