You can do better - generate synthetic data covering all topics. And to make it less prone to hallucination, use RAG or web search for reference material. The Phi-1.5 model was trained on 300B of synthetic tokens generated with chatGPT and it showed a 5x bump in efficiency, punching well above its line.

Synthetic data can be more diverse if you sample carefully with seeded concepts, and it can be more complex than average web text. You can even diff against a garden variety Mistral or LLaMA and only collect knowledge and skills they don't already have. I call this approach "Machine Study", where AI makes its own training data by studying its corpus and learning from other models.