A World-Action Model Learned From Its Own Verified Rollouts

EVO-WAM adapts a world-action model to unseen tasks without executing each candidate rollout externally. It selects prefixes that a vision-language model judges task-completing, checks video-action consistency with inverse dynamics and retrains iteratively on the verified subset. Across seven unseen RoboTwin 2.0 tasks, reported average success rose from 26.9% to 68.0% for Cosmos3 and from 28.5% to 46.4% for DreamZero. On three real long-horizon composites, Cosmos3 rose from 20.0% to 76.7%. The verifier stack can still inherit model error, so external validation remains necessary.