Inferring physical actions from visual observations is a fundamental capability for advancing machine intelligence in the physical world. Achieving this requires large-scale, open-vocabulary video action datasets that span broad domains. We introduce Action100M, a large-scale dataset constructed from 1.2M Internet instructional videos (14.6 years of duration), yielding O(100 million) temporally localized segments with open-vocabulary action supervision and rich captions. Action100M is generated by a fully automated pipeline that (i) performs hierarchical temporal segmentation using V-JEPA 2 embeddings, (ii) produces multi-level frame and segment captions organized as a Tree-of-Captions, and (iii) aggregates evidence with a reasoning model (GPT-OSS-120B) under a multi-round Self-Refine procedure to output structured annotations (brief/detailed action, actor, brief/detailed caption). Training VL-JEPA on Action100M demonstrates consistent data-scaling improvements and strong zero-shot performance across diverse action recognition benchmarks, establishing Action100M as a new foundation for scalable research in video understanding and world modeling.

翻译：从视觉观测中推断物理动作是推动机器智能在物理世界中发展的基础能力。实现这一目标需要大规模、开放词汇的视频动作数据集，涵盖广泛领域。我们提出了Action100M，这是一个基于120万条互联网教学视频（总时长14.6年）构建的大规模数据集，产生了约1亿个具有开放词汇动作监督和丰富描述文本的时间定位片段。Action100M由一个全自动流程生成，该流程（i）利用V-JEPA 2嵌入进行分层时间分割，（ii）生成组织为“描述树”的多层级帧与片段描述文本，（iii）通过推理模型（GPT-OSS-120B）在多轮自我优化程序下聚合证据，输出结构化标注（简洁/详细动作、执行者、简洁/详细描述）。在Action100M上训练VL-JEPA显示出持续的数据规模提升效应，并在多样化的动作识别基准测试中实现了强大的零样本性能，从而确立了Action100M作为视频理解与世界建模可扩展研究的新基础。