arxiv.orgpaper

SolarWM: Open Data and Scalable Training for Long-Horizon Video World Models

SolarWM is an open foundation for building interactive video world models, providing a reconfigurable multi-source data engine that unifies 1.43 million video clips from 10 datasets into a frame-aligned contract (visual observations, camera geometry, captions, quality metadata, provenance). Using shared camera-conditioning and training interfaces, the authors train four 5B-33B causal models built on Wan2.2, LTX-2.5, and MiniMax-H3 backbones via a three-stage recipe (bidirectional adaptation, teacher-forced autoregressive initialization, distribution matching distillation), enabling real-time interactive rollouts spanning minutes to hours despite training only on 5-second sequences. The project releases data, pipeline, training recipes, weights, and framework code for reproducible world-model research.