Title: Planning with Reasoning using Vision Language World Model
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: vision-language models, world moels

Abstract: Effective planning in the physical world requires strong world models, but models that can reason about high-level actions with semantic and temporal abstraction remain underdeveloped. We introduce the Vision Language World Model (VLWM), a foundation model trained for language-based world modeling on natural videos. Given visual observations, VLWM first infers the overall goal to be achieved and then predicts a trajectory composed of interleaved actions and world state changes. These targets are extracted by iterative LLM self-refinement conditioned on compressed future observations represented by a Tree of Captions. VLWM learns both an action policy and a dynamics model, enabling reactive system-1 plan decoding and reflective system-2 planning via cost minimization. The cost evaluates the semantic distance between hypothetical future states predicted by VLWM and the expected goal state, and is measured by a critic model trained in a self-supervised manner. VLWM achieves state-of-the-art performance on the Visual Planning for Assistance benchmark and our proposed PlannerArena human evaluations, where system-2 improves Elo score by 27% over system-1. It also outperforms strong VLM baselines on RoboVQA and WorldPrediction benchmarks.