Robot policies ship as weights alone: letting a world action model replay its own generated past cuts forgetting by over 90%
A world action model can regenerate complete rollouts of previously learned tasks from their instructions alone, and after truncation and physical-consistency filtering, replaying them cuts the forgetting of sequential fine-tuning by over 90% on four LIBERO suites and five real-world tasks without keeping any data from previous tasks.