Ask HN: Is synthetic data generation practical outside academia?

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!