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English(EN) Recursive Synthesis for Long-Horizon Terminal Tasks

新框架以每项0.05美元的价格生成37,000个AI代理任务

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了递归合成终端任务（RST）框架，旨在以显著降低的成本为终端代理生成长时域训练数据。该方法通过验证种子递归地合成新任务，扩展解决方案，重新对齐验证器，并在沙箱中进行验证，最终以每项约0.05美元的价格生成了超过37,000个任务。合成的任务在递归轮次中难度显著增加，导致DeepSeek-V4-Pro等模型的性能下降。使用RST生成的数据进行微调已显示出Qwen3.5模型在各种基准测试上的显著改进，证明了该框架在增强代理能力方面的实用性。 AI

影响 该框架可以显著降低创建AI代理训练数据的成本，从而可能加速开发和部署。

排序理由 该集群描述了一篇详细介绍用于生成AI训练数据的新颖框架的新研究论文。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新框架以每项0.05美元的价格生成37,000个AI代理任务

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该集群描述了一篇详细介绍用于生成AI训练数据的新颖框架的新研究论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhongzhi Li, Yucheng Shi, Zongxia Li, Ruhan Wang, Anhao Li, Zixun Huang, Junyao Yang, Lei Ke, Ninghao Liu, Haitao Mi, Leowei Liang ·

    面向长时域终端任务的递归合成

    arXiv:2608.05466v1 Announce Type: new Abstract: High-quality long-horizon training data for terminal agents is expensive to produce, often costing hundreds to thousands of dollars per task, because each task must keep the instruction, environment, reference solution, and verifier…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    面向长时域终端任务的递归合成

    High-quality long-horizon training data for terminal agents is expensive to produce, often costing hundreds to thousands of dollars per task, because each task must keep the instruction, environment, reference solution, and verifier mutually consistent. Human authoring does not s…

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