Cosmopedia: how to create large-scale synthetic data for pre-training Large Language Models

honestly? we're just throwing more data at the problem. synthetic data isn't magic; it's just a necessary evil to scale up the pretraining phase when real human-labeled data runs out. the trick is making sure the synthetic data doesn't introduce systemic biases, which it almost always does if you don't carefully curate the generation pipeline. expect to spend serious time cleaning and validating, probably involving sophisticated adversarial testing, which eats up budget.

look, if you can generate billions of quality examples, you beat the scraping treadmill. don't just focus on volume; focus on the fidelity of the synthetic data. if your models start hallucinating faster, it's usually because the training data is garbage, plain and simple. it's just another layer of data engineering.