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English(EN) SWE-Trail: History-Guided Trajectory Collection for Coding Agent Training

新的SWE-Trail方法增强了编码代理的训练数据收集

研究人员开发了SWE-Trail,一种用于为编码代理收集训练数据的新方法。该方法利用先前解决问题的历史数据来指导新轨迹的收集,而无需将此历史直接暴露给代理。这可以防止训练-测试不匹配,即代理可能会依赖实际使用中不可用的建议。当应用于从Ling-3.0-flash-base初始化的124B学生模型时,SWE-Trail在SWE-bench Multilingual和SWE-bench Pro基准测试中显著提高了问题解决率。 AI

影响 提高了编码AI代理训练数据收集的效率和有效性,可能带来更强大的代码生成工具。

排序理由 该集群描述了一篇详细介绍AI代理训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的SWE-Trail方法增强了编码代理的训练数据收集

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该集群描述了一篇详细介绍AI代理训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    SWE-Trail:用于编码代理训练的历史引导轨迹收集

    SWE-Trail improves trajectory collection for coding agents by using past rollouts to guide exploration without exposing history to the agent, yielding better-trained student models.