Synthetic Data
Definition
Training data generated by AI models rather than collected from humans. Synthetic data generation ranges from simple rephrasing to complex agentic traces with tool calls, multi-step reasoning, and domain-specific structure.
Why Synthetic Data
- Scale: Human annotation is expensive and slow. Synthetic generation can produce millions of examples cheaply.
- Specialization: Domain-specific data can be generated by fine-tuning a generator model — the generator knows domain rules that general annotators don’t.
- Control: Synthetic data can be generated to cover edge cases, adversarial examples, and rare scenarios underrepresented in organic data.
- Privacy: No sensitive user data required.
The Generator-Student Pipeline
Frontier labs now routinely use fine-tuned specialist models as generators:
- Fine-tune a generator on domain expertise
- Generator produces training data for a student model
- Student is evaluated; failures feed back to improve generator
This is how Fireworks AI supports Cursor’s Composer-2 RL training. See Fine-Tuning as a Service.
Agentic Data Quality Problem
From Vintage Data: Early agentic traces had ~30% faulty tool calls (malformed JSON). Getting reliable agentic behavior requires high-quality agentic traces — harder to generate than conversational data.
Limitations
- Distribution shift: Synthetic data can diverge from real-world distribution in subtle ways
- Error amplification: If the generator model has systematic errors, the student inherits them
- Formal rule accuracy: Transformers are good at learning formal rules from synthetic data — but only if the synthetic data has correct rules