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• Sequential Fine-Tuning with LoRA for Vision-Language-Action models achieves high plasticity with little to no catastrophic forgetting in continual reinforcement learning.result 90%
• Sequential Fine-Tuning with LoRA frequently outperforms more sophisticated continual reinforcement learning methods across benchmarks.result 85%
• The robustness of Sequential Fine-Tuning arises from synergy between large pretrained models, parameter-efficient adaptation, and on-policy RL.technical 80%
• Sequential Fine-Tuning with LoRA retains strong zero-shot generalization capabilities during continual learning.result 85%
• The combination of components in Sequential Fine-Tuning reshapes the stability-plasticity trade-off in continual learning.technical 80%
• Sequential Fine-Tuning is a powerful method for continual RL with Vision-Language-Action models.method 90%
• The study was conducted across three models and five challenging lifelong RL benchmarks.method 95%
• Conventional continual learning wisdom suggests naive Sequential Fine-Tuning leads to catastrophic forgetting.technical 85%

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