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今天的主题是：

VideoWorld: Exploring Knowledge Learning from Unlabeled Videos

Summary

The paper introduces VideoWorld, a novel approach to learning complex knowledge directly from unlabeled video data. It presents a video generation model that, unlike traditional language models, learns rules, reasoning, and planning skills solely from visual input, exemplified through tasks like video-based Go and robotic control. A key finding is that visual change representation is vital for knowledge acquisition, leading to the development of a Latent Dynamics Model (LDM) for enhanced efficiency. Remarkably, VideoWorld achieves high proficiency in Video-GoBench and demonstrates effective robotic control, rivaling oracle models. This research pioneers a new direction for AI learning, emphasizing the potential of visual data as a primary source of knowledge. The supplementary material gives additional details about the implementation and results.

该论文介绍了 VideoWorld，一种全新的方法，能够直接从无标签视频数据中学习复杂知识。论文提出了一种视频生成模型，不同于传统的语言模型，该模型仅依赖视觉输入学习规则、推理和规划能力，并通过视频版围棋（Video-based Go）和机器人控制等任务加以验证。研究的一个关键发现是，视觉变化的表示对于知识获取至关重要，据此提出了 潜在动力学模型（Latent Dynamics Model, LDM） 以提高学习效率。令人瞩目的是，VideoWorld 在 Video-GoBench 基准测试中表现出色，并在机器人控制任务上展现了可比肩先验模型（oracle models）的能力。这项研究开辟了 人工智能学习的新方向，强调了视觉数据作为知识主要来源的潜力。补充材料提供了关于实现细节和实验结果的更多信息。

原文链接：https://arxiv.org/abs/2501.09781