stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation

• URL: http://arxiv.org/abs/2602.08968v2
• Date: Tue, 17 Feb 2026 18:58:08 GMT
• Title: stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation
• Authors: Lucas Maes, Quentin Le Lidec, Dan Haramati, Nassim Massaudi, Damien Scieur, Yann LeCun, Randall Balestriero,
• Abstract summary: We introduce stable-worldmodel (SWM), a modular, tested, and documented world-model research ecosystem.<n>SWM provides efficient data-collection tools, standardized environments, planning algorithms, and baseline implementations.<n>We demonstrate the utility of SWM by using it to study zero-shot robustness in DINO-WM.
• Score: 46.55784222514516
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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