Efficient Adaptation of Large Pretrained Models for Decision-Making and Robotics: Introducing TAIL Framework

The author introduces the TAIL framework as an efficient adaptation method for pretrained decision-making models, emphasizing parameter-efficient fine-tuning techniques like LoRA. The core reasoning is to address challenges in continual learning by preserving original features while adapting to new tasks efficiently.