Are Video Reasoning Models Ready to Go Outside?

In real-world deployment, vision-language models often en-counter disturbances such as weather, occlusion, and camera motion.Under such conditions, their understanding and reasoning degrade sub-stantially, revealing a gap between clean, controlled (i.e., unperturbed)evaluation settings and real-world robustness. To address this limitation,we propose ROVA, a novel training framework that improves robustnessby modeling a robustness-aware consistency reward under spatio-temporalcorruptions. ROVA introduces a difficulty-aware online training strategythat prioritizes informative samples based on the model’s evolving capa-bility. Specifically, it continuously re-estimates sample difficulty via self-reflective evaluation, enabling adaptive training with a robustness-awareconsistency reward. We also introduce PVRBench, a new benchmark thatinjects real-world perturbations into embodied video datasets to assessboth accuracy and reasoning quality under realistic disturbances. Weevaluate ROVA and baselines on PVRBench, UrbanVideo, and VisBench,where open-source and proprietary models suffer up to 35% and 28%drops in accuracy and reasoning under realistic perturbations. ROVAeffectively mitigates performance degradation, boosting relative accuracyby at least 24% and reasoning by over 9% compared with baseline models(QWen2.5/3-VL, InternVL2.5, Embodied-R). These gains transfer to cleanstandard benchmarks, yielding consistent improvements.