[2603.03818] Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning

Computer Science > Machine Learning arXiv:2603.03818 (cs) [Submitted on 4 Mar 2026] Title:Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning Authors:Huihan Liu, Changyeon Kim, Bo Liu, Minghuan Liu, Yuke Zhu View a PDF of the paper titled Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning, by Huihan Liu and 4 other authors View PDF HTML (experimental) Abstract: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. In this work, we found that pretrained VLAs are remarkably resistant to forgetting compared with smaller policy models trained from scratch. Simple Experience Replay (ER) works surprisingly well on VLAs, sometimes achieving zero forgetting even with a small replay data size. Our analysis reveals that pretraining plays a critical role in downstream continual learning performance: large pretrained models mitigate forgetting with a small replay buffer size while maintaining strong forward learning capabilities. Furthermore, we found that VLAs can retain relevant knowledge from prior tasks despite p...