Synthetic data is known to be a super powerful tool for every level of the language modeling stack. It's documented as being used for expanding vanilla pretraining data and creating large swaths of fine-tuning data. Many, many more rumors surround its use, Anthropic's pretraining-scale constitutional AI, Mistral AI's first models being pretrained on OpenAI outputs, Q-star's hopes as OpenAI's remaining moat, and much more. The diversity of use cases for synthetic data makes planning around the role of synthetic data in solving specific goals.
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Source code: https://github.com/natolambert/interconnects-tools
Original post: https://www.interconnects.ai/p/frontiers-in-synthetic-data

00:00 Frontiers in synthetic data
01:14 1. Direct distillation is still king
02:54 2. Are Gemini Flash and Claude Haiku distilled?
04:03 3. Filtering prevents collapse
06:30 4. Synthetic data strategy taxes
07:32 5. Pros and cons of training on multi-output-source synthetic datasets
08:54 6. Structured synthetic data
09:42 7. Weak-to-strong generalization is maybe real
10:27 8. Creating synthetic prompts is overlooked again

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