English(EN) Tempo-Zero: Recursive Self-Play in Executable Worlds for Video Reasoning

Tempo-Zero 框架使用自我博弈进行高级视频推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 04:00

研究人员开发了 Tempo-Zero，一种用于视频推理的新型自我改进框架，该框架在可执行环境中利用递归自我博弈。该方法生成并解决自己的任务，无需手动标注，并克服了先前依赖嘈杂伪标签的自我博弈技术的局限性。Tempo-Zero 训练单个策略同时充当提问者和解决者，接收基于执行的奖励并随着时间的推移演化环境的动态。尽管仅在简单程序上进行训练，但与基础模型和现有的自我博弈基线相比，该框架在十二个真实世界视频基准测试中表现出色，尤其在时间定位任务上表现突出。 AI

影响引入了一种新颖的自我博弈方法，可以减少视频推理模型对标注数据的依赖。

排序理由该集群包含一篇详细介绍新研究框架及其在基准测试中表现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Jingmin Zhu, Yanyan Zou, Xiuyu Li, James Bailey, Dinh Phung, Tao Yu, Yunyun Hou, Jun Liu, Jun Zhou, Sirui Wang, Nan Duan, Jiachen Liu, Qiuhong Ke · 2026-10-26 04:00

Tempo-Zero：在可执行世界中进行递归自我博弈以实现视频推理

arXiv:2610.32433v1 Announce Type: new Abstract: Video reasoning requires understanding how events unfold over time, yet videos with accurate temporal annotations are expensive to collect. Self-play offers an alternative by letting a model generate and solve its own tasks without …

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Tempo-Zero：在可执行世界中进行递归自我博弈以实现视频推理

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