stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation
This work addresses reproducibility and standardization issues for researchers in world modeling, though it is incremental as it builds on existing paradigms without introducing new methods.
The paper tackles the lack of reusable and standardized implementations in world modeling research by introducing stable-worldmodel (SWM), a modular ecosystem with tools for data collection, environments, planning, and baselines, and demonstrates its utility in studying zero-shot robustness in DINO-WM.
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.