Synthetic data is increasingly being used to pre-train and fine-tune large models when real-world data is scarce, expensive, or constrained by privacy rules. The tradeoff is shifting from “can we generate enough data?” to “can we prove it’s accurate, unbiased, and uncontaminated?”
Synthetic data becomes a core ingredient in LLM pre-training and post-training
Synthetic data is being positioned as a practical way to scale AI model training while reducing reliance on sensitive or hard-to-collect real-world datasets. The source highlights a common pattern: teams use synthetic data to mirror key properties of real data (for coverage and diversity), then apply it across both pre-training and post-training workflows to improve model behavior without expanding privacy exposure.
Examples cited include Alibaba, Apple, and Google using synthetic data in training pipelines. Alibaba’s Qwen 2 is described as using synthetic data to augment training corpora for greater diversity. Apple’s AFM models are described as incorporating synthetic long-context data to improve performance on tasks requiring longer sequences. The piece also notes synthetic instruction-response pairs used for supervised instruction fine-tuning (SFT), again referencing Qwen 2 and Apple’s AFM as examples of post-training usage.
- For data leads: synthetic generation can reduce dependency on new real-data collection (and the procurement, labeling, and access-control work that comes with it), but it shifts the bottleneck to governance and measurement.
- For privacy and compliance: using less sensitive source data can lower compliance exposure, but only if teams can demonstrate synthetic outputs don’t leak sensitive information or re-identify individuals.
- For ML engineers: synthetic data can expand coverage (edge cases, long-context, instruction formats), but poor generation quality can hard-code errors or bias into the model and create brittle “looks good on paper” improvements.
- For evaluation owners: the piece flags evaluation contamination risk—if synthetic data overlaps with evals or encodes eval-like patterns, teams can inflate scores and ship regressions.