WALL-WM: Carving World Action Modeling at the Event Joints
Authors: name: Shalfun Li, name: Victor Yao, name: Charles Yang, name: Truth Qu, name: Regis Cheng, name: Ryan Yu, name: Howard Lu, name: Newton Von, name: Vincent Chen, name: Yohann Tang, name: Maeve Zhang, name: Ellie Ma, name: Gody Li, name: Sage Yang, name: Lorien Shu, name: J. W. Gao, name: Ethan Chen, name: Colin Ye, name: Yu Sun, name: Elise Mon, name: PS Zhang, name: Neo Li, name: Lily Li, name: James Wang, name: Ping Yang, name: Chris Pan, name: Lucy Liang, name: Hang Su, name: Roy Gan, name: Hao Wang, name: Qian Wang

WALL-WM is a World Action Model that shifts video-action learning from chunk-centric optimization to event-grounded Vision-Language-Action pretraining, using semantically coherent action events as the atomic unit of learning. Existing WAMs commonly initialize from multimodal or video foundation models and then optimize fixed-length action chunks conditioned directly on the current observation and instruction. Although convenient, this chunk-centric formulation creates a fundamental granularity mismatch. Language describes semantic goals and events, vision evolves through continuous scene dynamics, and actions operate at control-level timescales; forcing all three into the same fixed-length prediction window turns VLA training into short-horizon correlation fitting. WALL-WM addresses this mismatch by organizing both supervision and data around semantic events. Specifically, it pairs event-grounded VLA pretraining with a data ecosystem built from event-level captions and cluster-balanced sampling, enabling scalable learning over diverse behaviors, scenes, and task structures. From the same event-pretrained backbone, WALL-WM supports two complementary inference modes. The event mode consumes next-event descriptions and enables variable-length execution chunks, while the unified mode uses a VLM with Staircase Decoding to condition conventional fixed-length chunk inference while preserving a gradient-continuous VLA path. Together with Muon-optimizer-based large-scale pretraining infrastructure, WALL-WM provides a practical scale-up recipe for general-purpose WAMs. Experiments show that WALL-WM generalizes broadly across language, scenes, and tasks, achieving state-of-the-art performance in large-scale real-world generalization evaluation.