Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning

ArXi:2603.03818v2 Announce Type: replace Continual learning is a long-standing challenge in robot policy learning, where a policy must acquire new skills over time without catastrophically forgetting previously learned ones. While prior work has extensively studied continual learning in relatively small behavior cloning (BC) policy models trained from scratch, its behavior in modern large-scale pretrained Vision-Language-Action (VLA) models remains underexplored.