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English(EN) An Explicit Feedback Contract for Iterative ML-Agent Harnesses

新的反馈合同改进了机器学习代理模型开发

研究人员开发了一种新的迭代式机器学习代理显式反馈合同,以改进其模型开发过程。该合同规定了代理可以观察的内容、何时停止迭代以及何时可以访问测试集。通过仅在提交通过噪声阈值后才发布验证分数,该合同旨在防止代理自适应地选择验证集并过度优化。实验表明,这种方法减小了验证集-测试集之间的差距,略微提高了测试性能,并显著减少了代理所需的提交次数,从而防止它们在无界反馈通道中追逐噪声。 AI

影响 这种新的反馈机制通过防止过拟合和减少计算资源浪费,可能导致更高效、更可靠的机器学习代理开发。

排序理由 关于机器学习代理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

新的反馈合同改进了机器学习代理模型开发

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关于机器学习代理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kehui Yao, Chuanbo Hu, Jiao Chen, Ramin Giahi, Reza Yousefi Maragheh, Lalitesh Morishetti, Ziwei Hong, Yiyun Su, Jianpeng Xu, Evren Korpeoglu, Kannan Achan ·

    An Explicit Feedback Contract for Iterative ML-Agent Harnesses

    arXiv:2610.28798v1 Announce Type: new Abstract: Iterative ML agents develop models by editing code, running experiments, reading a validation score, and choosing what to try next. In current harnesses this loop is served by an exact validation score returned after every attempt, …