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English(EN) ARISE-RL: Agentic Rubric-Grounded Iterative Self-Evolution with Reinforcement Learning

新的ARISE-RL框架增强了开放式代理的强化学习能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出ARISE-RL，一个旨在利用强化学习改进开放式代理训练的新框架。该框架通过将任务/评分标准生成器与推理求解器相结合，解决了缺乏可验证答案和可扩展评分标准等挑战。ARISE-RL采用协同演化方法，其中生成器根据工具观察创建任务，求解器从评分标准满意度信号中学习。该系统还包含奖励门控自我演化蒸馏（RG-SED）来优化策略并减少对嘈杂指导的模仿。为了便于评估，研究人员开发了ECR-Bench，一个用于深度研究和多工具规划任务的基准套件，展示了ARISE-RL的先进性能。 AI

影响 该框架可能带来更强大、更鲁棒的开放式AI代理，提高在复杂推理和规划任务上的性能。

排序理由 该集群包含一篇详细介绍AI代理新框架和基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 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ARISE-RL框架增强了开放式代理的强化学习能力

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该集群包含一篇详细介绍AI代理新框架和基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Fanrui Zhang, Ruixue Ding, Qiang Zhang, Xi Chen, Boli Chen, Shihang Wang, Qiuchen Wang, Hongmin Zhan, Jinxin Bian, Li xingchao, Peijin Zheng, Hao cheng, Pengjun Xie, Kaipeng Zhang, Jiawei Liu, Zheng-Jun Zha ·

    ARISE-RL：基于规则的代理迭代自我演进强化学习

    arXiv:2609.01058v1 Announce Type: new Abstract: Training open-ended agents via reinforcement learning (RL) is hindered by the lack of verifiable gold answers and scalable rubrics. Moreover, even near the model's capability boundary, long-horizon open-ended agentic tasks often yie…

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