EvoSyn：通过演化生成空间实现闭环学习的智能体数据合成
原标题：Know What You Don't Know: Agentic Data Synthesis by Evolving Generation Space for Closed-Loop Learning
AI 摘要
论文提出 EvoSyn，一个由学习反馈驱动的多智能体框架，用于自演化的智能体数据合成。它围绕可修订的合成空间组织生成过程，通过 Designer 分配合成方向、Planner 将其落地为可执行经验，从而在空间内适配薄弱区域并扩展合成空间。实验表明，EvoSyn 在 -Bench 和 CAR-Bench 上相比最强合成基线提升了平均任务成功率，并产出更高质量、结构更广的合成经验。
正文节选
Know What You Don’t Know: Agentic Data Synthesis by Evolving Generation Space for Closed-Loop Learning Abstract The continued improvement of large language models increasingly depends on synthetic experience, yet effective data synthesis for interactive agents requires not only executable and verifiable trajectories, but also interactions that provide learning value for the current task agent. Existing methods increasingly incorporate environment feedback, data validation, or learner failures, b