ModelRefs / Synthetic Data — AI Glossary
Synthetic Data — AI Glossary
Training data generated by a model rather than collected from humans. Quality filtering and self-consistency checks are critical to avoid model collapse.
Overview
Modern frontier models rely heavily on synthetic data for reasoning traces, code, and underrepresented languages. Quality filtering and self-consistency checks are critical to avoid model collapse. Phi, Llama, and Qwen training pipelines all use large synthetic components.
Reference details
| Topic | training |
|---|---|
| Last reviewed | 2026-06-24 |
Related terms
Commonly confused with
Model-generated training data. Distillation is the case where the generator is deliberately a stronger model and the goal is transferring its capability; augmentation is perturbing real examples rather than inventing new ones. Two constraints travel with synthetic data regardless of label: provider terms often restrict training on outputs, and generated data inherits the generator's blind spots, so it cannot teach what the generator does not know.
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Frequently asked questions
What is Synthetic Data?
Training data generated by a model rather than collected from humans.
What concepts are related to Synthetic Data?
Closely related concepts include fine tuning, rlhf, sft.