Synthetic Data: When AI Trains on AI

Synthetic data is information generated by an algorithm rather than collected from the real world. In modern AI, this usually means using a large model to generate examples that train another model. Microsoft's Phi series famously used GPT-4 to generate textbook-quality training data.

Why labs want synthetic data

• Real data for rare scenarios (medical emergencies, edge cases) is scarce
• Scraped data carries legal and privacy risk
• Controlled generation lets you balance for underrepresented groups
• You can generate exactly the format you need (step-by-step reasoning, tool calls)
• It is often cheaper than paying humans to create examples

Real-world examples

The catch: model collapse

As AI-generated text floods the web, future models trained on scraped data may accidentally collapse. This is why labs now care deeply about dating their scrapes (ideally before 2022) and mixing synthetic with fresh human data carefully.

Distillation: the safest synthetic recipe

Distillation is a disciplined form of synthetic data. Use a big, smart teacher model to generate high-quality outputs. Use those outputs to train a smaller student model. This is how many efficient models (Claude Haiku, Gemini Flash, Llama 3.2-1B) get their intelligence.

# Simplified distillation loop
questions = load_hard_questions()

for q in questions:
    # Teacher (large model) generates rich reasoning
    teacher_answer = big_model.generate(q, reasoning=True)
    
    # Student learns to mimic
    student_loss = student_model.train_step(
        input=q,
        target=teacher_answer
    )

The big idea: synthetic data is not cheating, it is compression. The best synthetic data pipelines use large models to distill their knowledge into smaller, faster ones, and carefully guard against collapse.