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Synthetic Data

Models training on text other models wrote — and why this isn't always bad.

Mankaran Singh·Updated May 17, 2026

Where this idea lives

PREREQUISITESTOOLS THAT SHOW ITSynthetic DataHow AI Models Are TrainedHow AI Models Are Trained — From random noise to a model that can reason — the actual pipelineModel CollapseModel Collapse — What happens when models train on text written by other models — recursively.Fine-TuningFine-Tuning — Teaching a model new habits, not new knowledgeChatGPTChatGPTClaudeClaudeDeepSeekDeepSeekCommon misconception: Synthetic data is the lazy way to train.Common misconception: Curated synthetic data is the same as web data.Common misconception: Real data will run out and synthetic data won't replace it.
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You might think:Synthetic data is the lazy way to train.Curated synthetic data is the same as web data.Real data will run out and synthetic data won't replace it.

Common misconception

“Training on synthetic data is the lazy shortcut.”

Carefully designed synthetic data is sometimes better than scraped data — especially for tasks with verifiable answers (math problems, code with unit tests, instruction-following templates). What labs avoid is uncurated synthetic data (random model outputs treated as truth). The work is in the curation, not the avoidance.

Synthetic data is text generated by a model and then used to train another model (or even itself, in iterative refinement). It's now a standard part of every frontier training pipeline — not a desperate fallback.

Why it works

For verifiable tasks, you can validate synthetic examples before training:

  • Math. Generate a problem, generate a solution, check the answer. Keep only the verified pairs.
  • Code. Generate a function, generate tests, run the tests. Keep the ones that pass.
  • Instruction-following. Generate (instruction, response) pairs with a teacher model; have a stronger model rate them; keep top quality.

For non-verifiable tasks (creative writing, opinions), synthetic data is much riskier — without ground truth you can't filter for quality and errors compound.

Where it's transformative

The reasoning-model wave (o1, R1) leans heavily on synthetic data. Math and code problems with verifiable answers are an unlimited source. Generate millions, filter by correctness, train on the filtered set. Capability rises without needing new human data.

Where it bites

Uncurated synthetic data → model collapse. Repeated training-on-output loops narrow the distribution. The internet filling with AI text means filtering becomes a major effort for the next generation of training runs.

What to read next

Model collapse is the failure mode synthetic data produces when uncurated. Training is the broader process.

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intermediate
Read time5 min read
UpdatedMay 2026
Sources5

Read next

  1. Model Collapse →
  2. How AI Models Are Trained →
  3. Fine-Tuning →