Stanford Proposes CD Scaling Law Amid Data Scarcity

As high-quality training data becomes increasingly scarce, Stanford researchers proposed the Compute-Data (CD) scaling law to address the limitations of the traditional Chinchilla scaling law. This new approach quantifies the equivalent conversion between repeated data and compute, offering a novel framework for model scaling under data-limited pretraining scenarios.

2026-08-11 ~ 2026-08-12 · 2 related posts

• Compute Outpaces Data: Researchers Propose Compute-Data (CD) Scaling Laws — burny_tech · 2026-08-11
• Stanford Proposes CD Scaling Laws: Quantifying the Trade-off Between Repeated Data and Compute — StanfordAILab · 2026-08-12