English(EN) Turning Multi-Agent Communication into Token-Level Credit with On Policy Self Distillation

新方法通过基于Token的信用分配增强多智能体LLM通信

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-14 05:00

研究人员开发了一种名为OPSD4MAS的新方法，通过将通信转化为基于Token的信用分配来增强大型语言模型中的多智能体通信。该方法使用On Policy Self Distillation (OPSD) 提供细粒度的指导，从而改进单个智能体的决策。与现有的GRPO方法相比，该方法已显示出显著的改进，将数学pass@4分数提高了多达20个百分点。 AI

影响改进了复杂多智能体LLM系统中智能体的决策和性能。

排序理由学术论文，详细介绍了LLM中多智能体通信的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zixu Wang, Yu Zhao, Xing Zheng, Bingbing Xu, Tian Xueyun, JiaTang Luo, Weiqiang Wang, Bowen Song, Huawei Shen, Xueqi Cheng · 2026-11-14 05:00

将多智能体通信转化为基于策略的令牌级信用蒸馏

arXiv:2611.16206v1 Announce Type: new Abstract: Reinforcement learning improves problem solving in large language model multi-agent systems. Existing approaches overlook communication knowledge or compress it into scalar rewards, leaving coarse credit with little guidance on whic…

本文如何被排名

报道来源 [1]

将多智能体通信转化为基于策略的令牌级信用蒸馏

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