Co-Evolving Latent Action World Models

• URL: http://arxiv.org/abs/2510.26433v1
• Date: Thu, 30 Oct 2025 12:28:40 GMT
• Title: Co-Evolving Latent Action World Models
• Authors: Yucen Wang, Fengming Zhang, De-Chuan Zhan, Li Zhao, Kaixin Wang, Jiang Bian,
• Abstract summary: Adapting pre-trained video models into controllable world models via latent actions is a promising step towards creating generalist world models.<n>We propose CoLA-World, which for the first time successfully realizes this synergistic paradigm.<n>This unlocks a co-evolution cycle: the world model acts as a knowledgeable tutor, providing gradients to shape a high-quality LAM.
• Score: 57.48921576959243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adapting pre-trained video generation models into controllable world models via latent actions is a promising step towards creating generalist world models. The dominant paradigm adopts a two-stage approach that trains latent action model (LAM) and the world model separately, resulting in redundant training and limiting their potential for co-adaptation. A conceptually simple and appealing idea is to directly replace the forward dynamic model in LAM with a powerful world model and training them jointly, but it is non-trivial and prone to representational collapse. In this work, we propose CoLA-World, which for the first time successfully realizes this synergistic paradigm, resolving the core challenge in joint learning through a critical warm-up phase that effectively aligns the representations of the from-scratch LAM with the pre-trained world model. This unlocks a co-evolution cycle: the world model acts as a knowledgeable tutor, providing gradients to shape a high-quality LAM, while the LAM offers a more precise and adaptable control interface to the world model. Empirically, CoLA-World matches or outperforms prior two-stage methods in both video simulation quality and downstream visual planning, establishing a robust and efficient new paradigm for the field.

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