The Sequence Opinion #529: An Honest Debate About Synthetic Data for Foundation Model Training
Values, challenges and applications of one of the next frontiers in generative AI.
Foundation models have redefined what AI systems can do by being pretrained on vast, diverse datasets across text, images, and multimodal content. However, sourcing high-quality, real-world data at this scale poses major constraints in terms of cost, coverage, and control. Synthetic data—artificially generated through simulations, generative models, or programmatic logic—has emerged as a compelling alternative or complement for both pretraining and post-training.
This essay explores synthetic data's role in training foundation models, presenting core arguments for and against its use. It spans application domains like vision, NLP, and robotics, discusses real-world case studies, and reviews the dominant techniques for generating synthetic data. Finally, it evaluates where synthetic data excels and where it falls short, offering a framework for its effective use in large-scale AI pipelines.