Ask HN: Is synthetic data generation practical outside academia?
4 cpard 4 6/6/2025, 11:55:15 PM
I keep seeing synthetic data pipelines powering the latest LLM “breakthroughs”:
• TinyZero’s $30 fine-tuning workflow
• Sky-T1’s $450 reasoning-model build
• Meta AI’s Llama 3 herd (2024 paper detailing their synthetic-data training)
• Berkeley OpenThoughts (“Data Recipes for Reasoning Models”), published yesterday
There are also open-source toolkits you can experiment with:
https://github.com/meta-llama/synthetic-data-kit https://github.com/bespokelabsai/curator
But it still feels very research-oriented. I haven’t found many examples of these pipelines running in real-world products.
I’m curious:
1. Who is using synthetic-data pipelines in production today?
2. What tasks does it actually improve. E.g. fine-tuning smaller models for specific tasks?
Any real-world stories, pointers, or further reading would be hugely appreciated. Thanks!
historically used for processes which make use of time-series / simulations & modeling / forcasting. aka weather forcasting, related points in [0]
2) a) Testing with actual 'sensitive' data may not be possible for security reasons (aka payroll information, stock market price influences)[1]. b) insufficent/incomplete information. aka figure out how well what's known matches 'reality' and/or may suggest areas to look for 'missing' pieces in model.
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[0] : https://www.oreilly.com/library/view/practical-time-series/9...
[1] : https://www.k2view.com/what-is-synthetic-data-generation/
With synthetic data for large languages models it’s more about QA pairs and reasoning trails for solving complicated problems
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[1] : I told AI to make me a protein. Here's what it came up with : https://www.nature.com/articles/d41586-025-01586-y
[2] : AI Models for Protein Structure Prediction : https://frontlinegenomics.com/ai-models-for-protein-structur...
[3] : AI model deciphers the code in proteins that tells them where to go : https://news.mit.edu/2025/ai-model-deciphers-code-proteins-t...