Autodata：一個具代理性的資料科學家，以建立高品質合成資料

摘要

我們介紹Autodata，這是一種通用方法，使AI代理能夠充當數據科學家，構建高品質的訓練與評估資料。我們展示如何訓練（元優化）這樣的數據科學家代理，使其學習創建更強大的數據。我們描述了整體框架及具體的實作方法：Agentic Self-Instruct。我們在電腦科學研究任務、法律推理任務以及數學物件推理任務上進行實驗，與經典的合成資料集創建方法相比，我們獲得了更優的結果。此外，對數據科學家代理本身進行元優化，能帶來更顯著的性能提升。代理式數據創建提供了一種將增加的推理計算轉化為更高品質模型訓練的方式。總體而言，我們相信這一方向有潛力改變我們構建AI數據的方式。

English

We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data. We show how to train (meta-optimize) such a data scientist agent, so that it learns to create even stronger data. We describe the overall formulation, and a specific practical implementation, Agentic Self-Instruct. We conduct experiments on computer science research tasks, legal reasoning tasks and reasoning with mathematical objects, where we obtain improved results compared to classical synthetic dataset creation methods. Further, meta-optimizing the data scientist agent itself delivers an even larger performance uplift. Agentic data creation provides a way to convert increased inference compute into higher quality model training. Overall, we believe this direction has the potential to change the way we build AI data.