Watch and Learn: Learning to use computers from online videos
Abstract
Computer use agents (CUAs) need to plan long-horizon task workflows grounded in diverse, ever-changing applications and environments, but learning is hindered by the scarcity of large-scale, high-quality training data. Existing datasets are small, domain-specific, and costly to annotate, while current synthetic data generation methods often yield brittle, simplistic, or misaligned task demonstrations.
We introduce Watch & Learn (W&L), a framework that transforms human demonstration videos available in the Internet into executable UI trajectories at scale. Inspired by robotics, we train an inverse dynamics model that accurately predicts user actions from consecutive screens, bypassing the need for complex heuristics. To scale to the web, we curate a large state-transition corpus and design a retrieval framework that identifies relevant video tutorials, enabling automatic conversion of raw videos into structured UI trajectories without requiring manual annotations. Beyond training data, we show that the generated UI trajectories can also serve as in-context exemplars, providing CUAs with long-horizon priors and domain-specific knowledge at inference time.
On the challenging OSWorld and Mind2Web benchmarks, UI trajectories extracted with W&L consistently improve both general-purpose and state-of-the-art frameworks when used in-context, and delivers stronger gains for open-source models when used in training. These results highlight web-scale human demonstration videos as a practical and scalable foundation for advancing CUAs towards real-world deployment.
We introduce Watch & Learn (W&L), a framework that transforms human demonstration videos available in the Internet into executable UI trajectories at scale. Inspired by robotics, we train an inverse dynamics model that accurately predicts user actions from consecutive screens, bypassing the need for complex heuristics. To scale to the web, we curate a large state-transition corpus and design a retrieval framework that identifies relevant video tutorials, enabling automatic conversion of raw videos into structured UI trajectories without requiring manual annotations. Beyond training data, we show that the generated UI trajectories can also serve as in-context exemplars, providing CUAs with long-horizon priors and domain-specific knowledge at inference time.
On the challenging OSWorld and Mind2Web benchmarks, UI trajectories extracted with W&L consistently improve both general-purpose and state-of-the-art frameworks when used in-context, and delivers stronger gains for open-source models when used in training. These results highlight web-scale human demonstration videos as a practical and scalable foundation for advancing CUAs towards real-world deployment.
