DiLA: Disentangled Latent Action World Models

• URL: http://arxiv.org/abs/2605.15725v1
• Date: Fri, 15 May 2026 08:22:37 GMT
• Title: DiLA: Disentangled Latent Action World Models
• Abstract summary: Latent Action Models (LAMs) enable the learning of world models from unlabeled video.<n>LAMs face a fundamental trade-off between action abstraction and generation fidelity.<n>We introduce DiLA, a novel Disentangled Latent Action world model that aims to resolve this trade-off via content-structure disentanglement.
• Score: 11.259992289079534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Latent Action Models (LAMs) enable the learning of world models from unlabeled video by inferring abstract actions between consecutive frames. However, LAMs face a fundamental trade-off between action abstraction and generation fidelity. Existing methods typically circumvent this issue by using two-stage training with pre-trained world models or by limiting predictions to optical flow. In this paper, we introduce DiLA, a novel Disentangled Latent Action world model that aims to resolve this trade-off via content-structure disentanglement. Our key insight is that disentanglement and latent action learning are co-evolving: the predictive bottleneck inherent in latent action learning serves as a driving force for disentanglement, compelling the model to distill spatial layouts into the structure pathway while offloading visual details to a separate content pathway for generation. This synergy yields a continuous, semantically structured latent action space without compromising generative quality. DiLA achieves superior results in video generation quality, action transfer, visual planning, and manifold interpretability. These findings establish DiLA as a unified framework that simultaneously achieves high-level action abstraction and high-fidelity generation, advancing the frontier of self-supervised world model learning.

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