This paper explores Continual Reinforcement Learning (CRL) for large Vision-Language-Action (VLA) models, focusing on how these agents adapt to new tasks without losing prior knowledge. While traditional machine learning often suffers from catastrophic forgetting during sequential training, this research demonstrates that a simple Sequential Fine-Tuning approach remains remarkably effective. By combining pre-trained VLAs, on-policy reinforcement learning, and Low-Rank Adaptation (LoRA), the researchers found that models maintain high plasticity and strong zero-shot generalization. Their systematic study across multiple benchmarks reveals that this basic recipe often outperforms more complex, specialized CRL strategies. Ultimately, the source positions parameter-efficient fine-tuning as a scalable and stable foundation for developing lifelong embodied intelligence in robotic agents.