Synthetic data

As high-quality human text on the open web became a limiting resource, labs turned to generating training data with models themselves: worked solutions to math problems, code with passing tests, dialogue transcripts, and reasoning traces. Modern training pipelines mix curated human data with heavy synthetic components, especially for reasoning and coding skills.

Synthetic data powers distillation — a strong teacher generates examples a smaller student learns from — and fills coverage gaps such as rare languages, edge cases, and safety scenarios that natural data underrepresents. Quality control is the discipline: unfiltered model output fed back into training degrades quality, so pipelines verify, grade, and deduplicate aggressively.

For API customers, synthetic data generation is a recognizable cost line of its own: producing millions of training examples through a frontier model is exactly the kind of non-interactive bulk workload that batch APIs discount.