Autodata:让AI智能体成为数据科学家,自动构建高质量合成数据
阅读原文· arxiv.orgAutodata是一种通用方法,使AI智能体扮演数据科学家角色,自主构建高质量训练与评估数据。该方法支持对数据科学家智能体进行元优化,使其学会生成更优数据,具体实现为Agentic Self-Instruct。在计算机科学、法律推理及数学对象推理等任务上的实验表明,Autodata生成的合成数据集质量优于经典方法,且对智能体进行元优化能带来更显著的性能提升。该方向通过将推理计算转化为更高质量的训练数据,有望改变AI数据的构建方式。
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.