Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning

• URL: http://arxiv.org/abs/2603.11653v1
• Date: Thu, 12 Mar 2026 08:22:39 GMT
• Title: Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning
• Abstract summary: We find that simple Sequential Fine-Tuning (Seq. FT) with low-rank adaptation (LoRA) is remarkably strong.<n>Our results position Sequential Fine-Tuning as a powerful method for continual RL with Vision-Language-Action (VLA) models.
• Score: 26.103428215400697
• License: http://creativecommons.org/licenses/by/4.0/
• 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 three models and five challenging 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.

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