Vision-Language-Action (VLA) models show strong generalization for robotic control, but finetuning them with reinforcement learning (RL) is constrained by the high cost and safety risks of real-world interaction. Training VLA models in interactive world models avoids these issues but introduces several challenges, including pixel-level world modeling, multi-view consistency, and compounding errors under sparse rewards. Building on recent advances across large multimodal models and model-based RL, we propose VLA-MBPO, a practical framework to tackle these problems in VLA finetuning. Our approach has three key design choices: (i) adapting unified multimodal models (UMMs) for data-efficient world modeling; (ii) an interleaved view decoding mechanism to enforce multi-view consistency; and (iii) chunk-level branched rollout to mitigate error compounding. Theoretical analysis and experiments across simulation and real-world tasks demonstrate that VLA-MBPO significantly improves policy performance and sample efficiency, underscoring its robustness and scalability for real-world robotic deployment.
@article{arxiv.2603.20607,
title = {Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models},
author = {Zhilong Zhang and Haoxiang Ren and Yihao Sun and Yifei Sheng and Haonan Wang and Haoxin Lin and Zhichao Wu and Pierre-Luc Bacon and Yang Yu},
journal= {arXiv preprint arXiv:2603.20607},
year = {2026}
}