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新的Harness-RL框架增强了LLM代理训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了Harness-RL，一个旨在改进在多代理环境中运行的大型语言模型（LLM）代理训练的新强化学习框架。该框架解决了优化动作及其参数的共享序列级信号以及处理动态、相互依赖的会话等挑战。Harness-RL利用冲突感知策略优化（CAPO）和黑盒轨迹构建来解耦策略梯度并准确捕捉会话动态。在七个基准测试的评估中，Harness-RL使用Qwen2.5模型取得了强劲的性能，证明了其方法的有效性。 AI

影响 该框架可以提高复杂多代理LLM系统在长时任务中的训练效率和有效性。

排序理由 该集群是关于一篇详细介绍LLM代理训练新颖框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

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新的Harness-RL框架增强了LLM代理训练

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该集群是关于一篇详细介绍LLM代理训练新颖框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Yasha Wang ·

    Harness-RL：用于中心代理多智能体Harnesses的带动作参数解耦的黑盒强化学习

    Large language model agents increasingly solve long-horizon tasks through multi-agent harnesses in which a central agent coordinates specialized sub-agents, tools, and environments. Training the central policy in such a harness raises two challenges. First, an action label is a l…

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