Meta 搞了个 AutoData，让 AI agent 当数据科学家自动造训练数据，比自己写死的流水线强不少，在多个推理任务上效果更好。
Meta 的研究提出了 AutoData 框架，将 AI agent 作为数据科学家自动构建训练和评估数据。其实现 Agentic Self-Instruct 扩展了经典 Self-Instruct，增加了 agent 规划和工具使用。在计算机科学、法律推理和数学对象推理等任务上，AutoData 超越了传统合成数据方法。通过元优化训练数据生成 agent，还能获得更大性能提升。
New research from Meta. Building synthetic training data has stayed a fixed pipeline that you hand-...
New research from Meta. Building synthetic training data has stayed a fixed pipeline that you hand-tune and then freeze. Autodata casts an AI agent as a data scientist that builds training and evaluation data, with an implementation called Agentic Self-Instruct that extends classic Self-Instruct with agentic planning and tool use. Think of it as meta-optimization, where the data scientist agent is itself trained to produce stronger data, so the pipeline keeps improving instead of staying static. Across computer science research, legal reasoning, and reasoning over mathematical objects, it beats classical synthetic-data methods, and meta-optimizing the agent delivers an even larger uplift. Paper: arxiv.org/abs/2606.25996 Learn to build effective AI agents in our academy: academy.dair.ai 💬 15 🔄 11 ❤️ 64 👀 5831 📊 32 ⚡
- arXiv cs.LG06-24 16:08原文