World Action ModelsIntermediate
Completion Aware Guidance for World Action Models
This preprint introduces Completion Aware Guidance (CAG), a training-free sampling method that steers world action models to generate task-completing video-action chunks instead of plausible but incomplete continuations. On a RoboTwin 2.0 subset it lifts average success from 64.4% to 70.0%, and on DreamZero zero-shot simulation from 69% to 75%, while cutting task-incomplete imagination from 79% to 40%. It matters because it improves existing WAMs without retraining, reducing a distinct failure mode in short-horizon robot control.