Synthetic Data
Synthetic data in AI training refers to machine-generated examples used to supplement or replace human-produced training corpora. It has become central to both pre-training (extending data scale beyond what humans have written) and post-training (generating verifiable problem-answer pairs for rlvr).
Role in Pre-Training
jensen-huang identifies data exhaustion — the worry that human-written text would run out — as a previously cited blocker to ai-scaling-laws, now resolved by synthetic data (fridman-huang-2026-nvidia-ai-revolution):
“The amount of data we use to train models will continue to scale to the point where data is limited by compute, not by human production.”
The mechanism: AI systems can generate training data from ground-truth sources (textbooks, code, mathematics), validate it, and feed it back into training loops. High-quality synthetic data amplifies the effective dataset size without requiring more human writing.
nathan-lambert and sebastian-raschka refine this picture (fridman-lambert-raschka-2026-state-of-ai): data quality is now the binding constraint, not volume. Synthetic data is valuable precisely because curation pipelines can filter for high-quality examples at machine speed. The most valued natural sources are OCR-extracted PDFs from arXiv and Semantic Scholar — dense, factually-accurate text written for experts — rather than raw web crawl data.
Role in Post-Training: RLVR
Synthetic data is even more critical for rlvr (Reinforcement Learning with Verifiable Rewards). The RLVR pipeline requires:
- A verifiable question (e.g., a math problem, code specification)
- A ground-truth answer or executable test suite
- The model’s answer, which is checked mechanically (no human in the loop)
Generating sufficient verifiable question-answer pairs at the scale needed for RL training requires synthetic construction — human problem-writers cannot produce millions of varied, difficulty-graded problems manually. Synthetic math and code problems dominate current RLVR training sets.
Mid-Training Synthetic Data
Between pre-training and post-training, a mid-training phase uses the same next-token-prediction algorithm but on capability-specific synthetic data: long reasoning traces, tool-use demonstrations, long-context examples. This injects skills the base pre-training corpus does not naturally contain, and is required for RLVR to work (the model needs exposure to relevant problem formats before RL gradient updates can refine its strategy).
Data Quality vs. Volume
Pre-2023 scaling focused primarily on how many tokens to train on. The Chinchilla paper (2022) shifted attention to compute-optimal data/parameter ratios. By 2025–2026, the frontier has shifted again: quality filtering of synthetic data is the key differentiator. Qwen (Alibaba) reportedly trained on 50T tokens; rumoured frontier labs up to 100T — but the gains come from curation pipelines that select for density of factual and logical content, not from raw token count.
Risks
- Synthetic data loops — training on model-generated data recursively can degrade diversity and introduce systematic errors; quality gates are essential
- Training data rights — synthetic data generated from licensed corpora may carry derivative copyright concerns; anthropic’s $1.5B training data lawsuit (2026) illustrates the legal risk of sourcing decisions in this space
Sources: fridman-huang-2026-nvidia-ai-revolution | fridman-lambert-raschka-2026-state-of-ai