Title: WMPO: World Model-based Policy Optimization for Vision-Language-Action Models
Primary area: applications to robotics, autonomy, planning
Keywords: World Models; Vision-Language-Action Models; Reinforcement Learning

Abstract: Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation, but their reliance on expert demonstrations limits their ability to learn from failures and perform self-corrections. 
Reinforcement learning (RL) addresses these through self-improving interactions with the physical environment, but suffers from high sample complexity on real robots.
We introduce World-Model-based Policy Optimization (WMPO), a principled framework for on-policy VLA RL without interacting with the real environment.
In contrast to widely used latent world models, 
WMPO focuses on pixel-based predictions that align the "imagined" trajectories with the VLA features pretrained with web-scale images.
Crucially, WMPO enables the policy to perform on-policy GRPO that provides stronger performance than the often-used off-policy methods.
Extensive experiments in both simulation and real-robot settings demonstrate that WMPO (i) substantially improves sample efficiency, (ii) achieves stronger overall performance, (iii) exhibits emergent behaviors such as self-correction, and (iv) demonstrates robust generalization and lifelong learning capabilities.

TL;DR: WMPO is a world-model-based framework that conducts reinforcement learning for Vision-Language-Action policies entirely in imagination.