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@InProceedings{hu_crlvla2026,
  author   = {Jiaheng Hu and Jay Shim and Chen Tang and Yoonchang Sung and Bo Liu and Peter Stone and Roberto Martin-Martin},
  title    = {Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning},
  booktitle = {Reinforcement Learning Journal},
  year     = {2026},
  month    = {August},
  location = {Montreal, Canada},
  abstract = {Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that can adapt in openended, evolving environments. However, conventional wisdom from continual learning suggests that naive Sequential Fine-Tuning (Seq. FT) leads to catastrophic forgetting, necessitating complex CRL strategies. In this work, we take a step back and conduct a systematic study of CRL for large pretrained VLAs across diverse lifelong RL benchmarks. We find that, contrary to established belief, simple Seq. FT with low-rank adaptation (LoRA) is remarkably strong: it achieves high plasticity, exhibits little to no forgetting, and retains strong zero-shot generalization, frequently outperforming more sophisticated CRL methods. Through detailed analysis, we show that this robustness arises from a synergy between the large pretrained model, parameter-efficient adaptation, and on-policy RL. Together, these components reshape the stability-plasticity trade-off, making continual adaptation both stable and scalable. Our results position Sequential Fine-Tuning as a powerful method for continual RL with VLAs and provide new insights into lifelong learning in the large model era. Code is available at github.com/UT-Austin-RobIn/continual-vla-rl.},
}