elvis@omarsar0 · XMeta 发布新研究 Autodata，提出 Agentic Self-Instruct 方法。该方法将 AI 智能体视为数据科学家，通过智能体规划与工具使用，替代传统手工调优后固定的合成数据流水线。该智能体自身可通过元优化持续改进，从而生成更强训练数据。实验在计算机科学、法律推理、数学对象推理三个领域均超越经典合成数据方法，且元优化带来更大提升。论文见 arxiv。
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： https://arxiv.org/abs/2606.25996