Abstract
World Models have emerged as a powerful paradigm for learning compact, predictive representations of environment dynamics, enabling agents to reason, plan, and generalize beyond direct experience. Despite recent interest in World Models, most available implementations remain publication-specific, severely limiting their reusability, increasing the risk of bugs, and reducing evaluation standardization. To mitigate these issues, we introduce stable-worldmodel (SWM), a modular, tested, and documented world-model research ecosystem that provides efficient data-collection tools, standardized environments, planning algorithms, and baseline implementations. In addition, each environment in SWM enables controllable factors of variation, including visual and physical properties, to support robustness and continual learning research. Finally, we demonstrate the utility of SWM by using it to study zero-shot robustness in DINO-WM.
Abstract (translated)
世界模型（World Models）作为一种强大的范式，已被应用于学习环境动态的紧凑、预测表示，从而使得智能体能够进行推理、规划，并超越直接经验进行泛化。尽管近年来人们对世界模型的兴趣不断增加，但大多数现有的实现仍然局限于特定的出版物中，这极大地限制了它们的再利用性，增加了出现错误的风险，并且降低了评估标准的一致性。 为了解决这些问题，我们推出了stable-worldmodel（SWM），这是一个模块化的、经过测试和文档化的世界模型研究生态系统。它提供了高效的收集工具、标准化的环境、规划算法以及基线实现方法。此外，SWM中的每个环境都支持可控的变化因素，包括视觉和物理特性，以促进稳健性和持续学习的研究。 最后，我们通过使用SWM来研究DINO-WM在零样本鲁棒性方面的表现，展示了SWM的有效性。
URL
https://arxiv.org/abs/2602.08968
