Invent-A-Dataset generates post-training datasets from zero data, reporting 17% relative quality gains and 19% relative diversity gains
This technical report introduces Invent-A-Dataset, a prompt-based system that goes from a dataset description to realistic and large-scale post-training datasets; evaluated in a zero-data regime against five frontier model APIs (Anthropic, Google, OpenAI, DeepSeek, Zai) across eight task types and dataset sizes up to 20K samples, it reports the highest quality (17% relative gains) and the most diverse samples (19% relative gains), with its diversity advantage widening with scale (from parity at 200 samples to 37% relative gains at 20K samples), translating into downstream training gains and higher rankings for its fine-tune across post-trained model architectures.