Training
Synthetic Data
AI-generated training data — used when real data is scarce, expensive, sensitive, or simply not high-enough quality.
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In plain English
Synthetic data is training data produced by another AI model (or a simulator) rather than collected from the real world. It's become a load-bearing part of frontier training: most current top models use significant amounts of synthetic data in both pretraining and post-training.
Why teams generate it:
- Scarcity — for rare or expert tasks, real examples are hard to find
- Quality control — synthetic data can be curated to be cleaner than scraped data
- Privacy — replace PII-heavy real records with synthetic equivalents
- Coverage — generate edge cases the real distribution under-represents
- Distillation — a big model labels examples for a small model (knowledge distillation)
Where it's used:
- Pretraining — Phi-4 famously trained heavily on synthetic textbooks
- Reasoning models — chain-of-thought traces are largely synthetic
- Robotics — sim-to-real pipelines generate training scenes
- Fine-tuning — generate task-specific instruction-response pairs at volume
Risks: "Model collapse" — training on synthetic data from a previous generation can compound errors over time. Frontier labs mitigate this by mixing synthetic with high-quality real data and by careful filtering.
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Related terms
Training Data
The dataset an AI model learns from — its quality, diversity, and biases directly shape what the model can do and how well it does it.
Pre-training
The first and most expensive phase of building a model — learning language and world knowledge by predicting the next token across trillions of words.
Post-training
Everything done to a model after pretraining — fine-tuning, RLHF, DPO, safety training — to turn a raw base model into a usable product.
Fine-tuning
Further training a pre-trained AI model on your own data to specialise it for a specific task or style.
Knowledge Distillation
Training a small "student" model to imitate a large "teacher" model — capturing most of the teacher’s capability at a fraction of the size and cost.
Foundation Model
A large, general-purpose AI model trained on broad data that can be adapted (via prompting or fine-tuning) to many downstream tasks.
Back to glossaryLast reviewed May 2026