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@InProceedings{zzwang_flam,
  author   = {Zizhao Wang and Chang Shi and Jiaheng Hu and Kevin Rohling and Roberto Martín-Martín and Amy Zhang and Peter Stone },
  title    = {Factored Latent Action World Models},
  booktitle = {International Conference on Machine Learning},
  year     = {2026},
  month    = {July},
  location = {Seoul, South Korea},
  abstract = {Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent actions provide a natural interface for users to iteratively generate and manipulate videos. However, most existing approaches rely on monolithic inverse and forward dynamics models that learn a single latent action to control the entire scene, and therefore struggle in complex environments where multiple entities act simultaneously. This paper introduces Factored Latent Action Model (FLAM), a factored dynamics framework that decomposes the scene into independent factors, each inferring its own latent action and predicting its own next-step factor value. This factorized structure enables more accurate modeling of complex multi-entity dynamics and improves video generation quality in action-free video settings compared to monolithic models. Based on experiments on both simulation and real-world multi-entity datasets, we find that FLAM outperforms prior work in prediction accuracy and representation quality, and facilitates downstream policy learning, demonstrating the benefits of factorized latent action models.},
}