Synthetic Data

What Synthetic Data is

Synthetic data can be simulator output, procedurally generated scenes, statistically matched tabular records, or text and images produced by another model.

How it works

Generators are validated by checking that models trained on synthetic data transfer to real evaluation sets, and that privacy properties actually hold. Mixing a modest share of synthetic with real data is more reliable than going fully synthetic.

Why it matters

It addresses rare events, privacy restrictions and the labelling bottleneck at once, and has become a standard part of instruction-tuning pipelines.

Common uses

• →Rare failure scenarios for autonomous driving
• →Privacy-preserving healthcare datasets
• →Instruction and reasoning data for model tuning
• →Load and edge-case testing