Revisiting Scaling Laws: Data Quality Emerges as Key to Model Breakthroughs
auto_grad_ · x · 2026-08-19
The author discusses factors governing model capability extraction, noting that simply scaling parameters has hit a bottleneck, requiring a shift to data quality. Citing Jie Tang, it emphasizes balancing parameters, data volume, and compute allocation. Referencing a new paper, it introduces a dimensionless data-quality parameter Q to extend the Chinchilla framework for modeling data quality's role in pretraining.
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