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English(EN) ParetoSaddler: Pareto-Guided Optimization of Agent Harnesses for Quality and Cost

ParetoSaddler 优化 LLM 智能体组合，兼顾质量与成本

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了一种新颖的优化大型语言模型 (LLM) 智能体组合的方法 ParetoSaddler。该方法将中间候选者晋升与最终部署选择分开，从而可以联合优化响应质量和成本。通过使用贝叶斯自举帕累托支持，ParetoSaddler 识别出在质量或成本方面显示出可信进展的候选者，保留了可能被丢弃的有价值的选项。在 ClawMark 和 GAIA2 基准测试上的评估表明，在降低每项任务成本的同时，质量也得到了显著提高。 AI

影响 该方法通过在不改变核心模型权重的情况下优化其性能，有望实现更高效、更具成本效益的 LLM 智能体部署。

排序理由 该集群包含一篇详细介绍 LLM 智能体组合优化新方法的论文。

在 arXiv cs.MA (Multiagent) 阅读 →

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ParetoSaddler 优化 LLM 智能体组合，兼顾质量与成本

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报道来源 [3]

  1. arXiv cs.CL TIER_1 English(EN) · Xingzhi Qian, Sungho Park, Rongyuan Tan, Jianbo Yu, Jue Zhang, Lorenzo Cavallaro, Menglin Xia, Yanzhi Chen, Xinnuo Xu, Yu Kang, Xinran Zheng, Gagan Somashekar, Xuchao Zhang, Chetan Bansal, Qingwei Lin, Victor R\"uhle ·

    ParetoSaddler: 代理优化器在质量和成本方面的 Pareto 指导优化

    arXiv:2611.02079v1 Announce Type: cross Abstract: Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality. Extending them to joint quality--cost optimization introduces a challenge: applyi…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Victor Rühle ·

    ParetoSaddler: Pareto-Guided Optimization of Agent Harnesses for Quality and Cost

    Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality. Extending them to joint quality--cost optimization introduces a challenge: applying the deployment preference during intermediate u…

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

    ParetoSaddler: 代理绑定的帕累托最优引导优化，兼顾质量与成本

    ParetoSaddler improves LLM agent harnesses by separately promoting intermediate candidates via Bayesian bootstrap Pareto support and selecting final deployments, boosting quality while reducing cost.

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