The proposed methods, GOFT and qGOFT, can be extended to handle more complex fine-tuning scenarios by incorporating them into multi-task learning or domain-specific adaptation frameworks. In multi-task learning, the methods can be adapted to simultaneously fine-tune a model on multiple tasks by incorporating task-specific adapters or heads. This approach allows the model to learn representations that are optimized for different tasks while still preserving the pretrained knowledge. Additionally, in domain-specific adaptation, the methods can be tailored to adapt a model to a specific domain by fine-tuning on domain-specific data with the goal of improving performance on tasks within that domain. By adjusting the regularization parameters and tuning strategies, the methods can be customized to effectively adapt to the nuances of different tasks or domains.