English(EN) SocialRL: Refining LLMs' Social Intelligence through Multi-turn Reinforcement Learning and Reward Design

新的SocialRL框架通过多轮强化学习增强大型语言模型的社交智能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了SocialRL，一个旨在通过多轮强化学习和复杂的奖励设计来增强大型语言模型（LLMs）社交智能的新框架。该方法解决了现有方法只关注单轮交互和即时奖励的局限性，这些方法可能导致次优的长期规划。SocialRL利用近端策略优化（PPO）在多轮中传播延迟奖励，从而实现更有效的长时程规划。该框架包含六个不同的奖励维度，包括目标推进和关系协调，并采用动态奖励模型，根据对话阶段调整优先级，最终使目标达成率平均提高9.2个百分点。 AI

影响这项研究可能带来更有效、更值得信赖的AI协作者，它们能够在延长的对话中驾驭复杂的社交动态。

排序理由该集群描述了一篇详细介绍改进LLM社交智能的新框架的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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本文如何被排名

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Newsworthiness bucket: Tool; 该集群描述了一篇详细介绍改进LLM社交智能的新框架的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Jianing Wang, Xintao Wang, Aili Chen, Jie Shi, Hongcheng Guo, Jun Gao, Wenxuan Zhao, Chengkun Lang, Yuanli Guo, Yanghua Xiao · 2026-09-10 04:00

SocialRL：通过多轮强化学习和奖励设计精炼LLM的社交智能

arXiv:2609.09764v1 Announce Type: new Abstract: Social intelligence enables agents to read social context, infer intent, and adapt over sustained dialogue. As language models become autonomous collaborators, it is central to building effective and trustworthy human-AI interaction…

本文如何被排名

报道来源 [1]

SocialRL：通过多轮强化学习和奖励设计精炼LLM的社交智能

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