新的SteerablePlex方法增强了全双工对话式AI的控制能力

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

研究人员开发了SteerablePlex，一种用于提高全双工对话模型控制力的新方法。这些模型能够同时进行听和说，但在对话历史增长时，常常难以维持对话场景。为解决此问题，创建了一个新的基准SimIF-Bench，以评估指令遵循能力。SteerablePlex方法利用了Group Reward-Decoupled Normalization Policy Optimization (GDPO)训练方法，使模型能够遵循文本指令，同时保留其轮流发言的能力。这种增强的控制力使得SteerablePlex成为比现有开源模型和GPT-Realtime更可靠的用户模拟器。 AI

影响增强了全双工模型的可控性，可能改进用户模拟和对话式AI应用。

排序理由该集群描述了一篇介绍对话式AI模型新方法和新基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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Newsworthiness bucket: Tool; 该集群描述了一篇介绍对话式AI模型新方法和新基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Haolong Zheng, Maike Z\"ufle, Dominik Mach\'a\v{c}ek, Peter Pol\'ak, Xulin Fan, Xavier Sumba, Siyin Wang, Ond\v{r}ej Klejch, Mark Hasegawa-Johnson · 2026-10-09 04:00

SteerablePlex：我们能否引导全双工模型？

arXiv:2610.12201v1 Announce Type: cross Abstract: Full-duplex speech models can listen and speak simultaneously, enabling natural interaction, but become increasingly difficult to control as the conversation history grows. When used as user simulators, this lack of control can ca…

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SteerablePlex：我们能否引导全双工模型？

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