Synthetic Data

The Data Wall

Large language models are trained on text from the internet—books, articles, code, conversations. But the supply of high-quality human-generated text is finite. By some estimates, frontier models have already consumed most of the high-quality text available online. This creates the "data wall": a point where further scaling requires either new sources of data or better ways to use existing data.

Synthetic data offers a path around this wall. Instead of collecting more human data, AI systems generate their own training data—creating examples, solving problems, producing explanations, and simulating scenarios that can then be used to train other models.

How Synthetic Data Works

Several approaches have proven effective:

Model distillation: A large, capable model generates responses that are used to train a smaller, cheaper model. The smaller model learns to approximate the larger model's capabilities at a fraction of the compute cost. This technique powers many of the efficient open-source models available today.

Self-play and self-improvement: A model generates problems, attempts to solve them, verifies its solutions, and trains on the successful ones. This was central to DeepMind's AlphaGo and AlphaZero, which achieved superhuman performance in Go and chess by playing millions of games against themselves.

Curriculum generation: AI generates training examples of calibrated difficulty, creating structured learning progressions. Microsoft's Phi series demonstrated that small models trained on carefully curated synthetic "textbook" data can match much larger models trained on raw web data.

Data augmentation: Existing data is transformed, paraphrased, translated, or extended by AI to increase dataset size and diversity without collecting new human data.

Why It Matters

Synthetic data addresses several critical problems:

Scale: When human data runs out, synthetic data lets training continue. This is becoming essential as models grow larger and data-hungrier.

Quality control: Synthetic data can be filtered, verified, and curated more systematically than web-scraped data. Errors can be corrected, biases can be measured and reduced, and difficulty can be calibrated.

Privacy: Synthetic data that mimics the statistical properties of real data without containing actual personal information can enable training in sensitive domains like healthcare and finance.

Cost: Generating synthetic data is often cheaper than collecting, cleaning, and annotating real-world data at scale.

Coverage: Synthetic data can fill gaps in real data—generating examples of rare events, edge cases, or scenarios that are underrepresented in existing datasets.

Risks and Limitations

Synthetic data is not a free lunch:

Distribution drift: If models train primarily on outputs from other models, the data distribution can drift away from reality. Subtle biases and errors compound across generations, a phenomenon called model collapse.

Hallucination amplification: If a model generates confident but incorrect information and that information is used for training, the error becomes embedded in the next generation of models.

Diversity loss: Models tend to generate text that clusters around common patterns. Training on such data can reduce the diversity and creativity of outputs over time.

Evaluation difficulty: When training data is synthetic, it becomes harder to evaluate whether models are genuinely capable or merely good at mimicking the patterns in their training data.

The Role in Scaling AI

Despite the risks, synthetic data is becoming central to AI development. The reasoning capabilities of frontier models in 2025-2026 were substantially improved through synthetic chain-of-thought data—models generating step-by-step reasoning traces that were then used for training.

Reinforcement learning from AI feedback (RLAIF) uses AI-generated evaluations instead of human ratings, dramatically reducing the cost of alignment training. Constitutional AI, developed by Anthropic, uses this approach to train models that are both helpful and harmless.

The trajectory is clear: as human data saturates, synthetic data will comprise an increasing share of training data. The challenge is maintaining quality, diversity, and grounding in reality as the proportion of synthetic data grows.