English(EN) SWAM: Scaling World Action Models with Cross-Modal Alignment

SWAM模型通过跨模态对齐推进世界动作模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-09 00:00

研究人员开发了SWAM，一种通过关注语言、视频和动作之间的跨模态对齐来扩展世界动作模型（WAMs）的新方法。该研究调查了不同的数据和监督策略如何影响WAM性能，特别是在动作学习过程中保持语言-视频对齐。通过策划大型数据集并试验各种训练方法和架构，SWAM展示了改进的控制泛化能力，并在三项现实世界任务中取得了40.00%的成功率，显著优于现有基线。 AI

影响这项研究可能带来更强大的AI代理，它们能够根据自然语言指令理解和执行复杂动作。

排序理由该项目是一篇研究论文，详细介绍了一个新模型和方法论。[lever_c_demoted from research: ic=1 ai=1.0]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-11-09 00:00

SWAM：通过跨模态对齐扩展世界动作模型

World action models jointly predict video and actions conditioned on language. Existing studies focus primarily on architectures and training mechanisms, typically treating language-video alignment acquired through video pretraining as an established foundation. However, introduc…

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SWAM：通过跨模态对齐扩展世界动作模型

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