Training
Synthetic Data
Also known as: AI-generated training data
Artificial training data created by AI or simulation instead of collected from the real world; increasingly important for training modern models.
Synthetic data is training data that is generated rather than collected, made by AI or simulations instead of taken from the real world. It is useful when real data is scarce, private or expensive: you can create examples to teach a model. Used well, it makes models better and protects privacy; used carelessly (training AI only on AI output) it can backfire (see model collapse). The key is quality and mixing it with real data.
Synthetic data is artificially generated training data, often produced by an LLM or a simulator, used to train or fine-tune models. It has become a major ingredient in modern AI.
Why it is used
- Scarcity: create examples where real data is limited (rare cases, new tasks).
- Privacy: avoid using sensitive real data.
- Quality control: generate clean, well-labelled, targeted examples, a big reason capable small models exist (distillation is a related idea).
The caution
Relying too heavily and recursively on AI-generated data can degrade models (model collapse). Good practice mixes synthetic with real data and filters carefully for quality.
Synthetic data is generated (by models, simulators, or procedural methods) rather than observed, used across pretraining, instruction tuning and evaluation. High-quality curated/synthetic corpora have driven strong compact models (e.g. Phi-3; Abdin et al. 2024), and distillation, training on a stronger model's outputs, is a widely used synthetic-data technique. Benefits include coverage of rare/edge cases, controllable difficulty and labels, privacy preservation, and cost.
Risks and constraints: recursive training on generated data risks model collapse (tail loss, reduced diversity); synthetic data can inherit and amplify the generator's biases and errors and impose a quality ceiling; and licensing/terms may restrict distilling from proprietary models. Mitigations centre on provenance tracking, filtering and verification (ideally against ground truth or execution feedback), diversity control, and blending with fresh real data. The practical stance is that synthetic data is a powerful tool whose value depends on curation and mixing discipline, not a substitute for real data.