Synthetic data: generate examples, then earn their trust

Synthetic data is produced through a generation process rather than collected directly as the original observation. For language-model training, another model can generate instructions, responses, explanations or exercises. Simulators and procedural programs can produce other kinds of examples.

The benefit is control: a generator can target missing cases, vary formats or create many examples of a skill that is scarce in collected data. The difficult part is whether those examples contain the right learning signal.

Generation is not verification

Self-Instruct generated instruction examples and filtered invalid or overly similar candidates before fine-tuning. Textbooks Are All You Need combined selected web material with generated educational content and exercises. These are examples of constructed training pipelines, not evidence that every generated answer is a reliable target.

A code example can be checked against independent tests. A factual answer needs evidence beyond the generator repeating its own claim. A fluent explanation can still have a wrong intermediate step.

Repetition can narrow the distribution

Repeatedly replacing training material with model-generated samples can lose rare patterns and amplify errors. Research on model collapse studies such feedback loops. The outcome depends on the data mixture, filtering and training process; it does not imply all synthetic data is harmful.

Useful synthetic-data work preserves provenance, checks diversity and evaluates on material that was not produced by the same pipeline. Otherwise a model can appear to improve simply because the evaluation shares the generator's habits.

Distillation is a related training use of another model's signals. Synthetic data describes where examples came from; it does not by itself specify how the student learns from them.