Factored Latent Action World Models

• URL: http://arxiv.org/abs/2602.16229v1
• Date: Wed, 18 Feb 2026 07:08:14 GMT
• Title: Factored Latent Action World Models
• Authors: Zizhao Wang, Chang Shi, Jiaheng Hu, Kevin Rohling, Roberto Martín-Martín, Amy Zhang, Peter Stone,
• Abstract summary: Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning.<n>This paper introduces Factored Latent Action Model (FLAM), a factored dynamics framework that decomposes the scene into independent factors.
• Score: 39.60866765151469
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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