Core Viewpoint - The article discusses the evolution of model adaptation techniques in the context of large pre-trained models, emphasizing a shift from parameter-centric adaptation to reprogrammability-centric adaptation, which allows for efficient task adaptation without modifying model parameters [5][9]. Group 1: Transition in Model Training Paradigms - The adaptation paradigm has fundamentally shifted from traditional parameter adjustment to a focus on model reprogrammability, enabling the reuse of pre-trained models across various tasks with minimal computational overhead [5][9]. - The new approach emphasizes modifying the task presentation rather than the model itself, allowing a single frozen model to handle multiple tasks by changing the interaction method [9]. Group 2: Efficiency Advantages of Reprogrammability - Empirical data shows that reprogrammability-centric adaptation (RCA) significantly outperforms parameter-centric adaptation (PCA) in terms of parameter efficiency, requiring 2-3 orders of magnitude fewer parameters for task adaptation [11][12]. - RCA enables adaptation in resource-constrained environments and supports simultaneous adaptation to multiple tasks without catastrophic forgetting, making it increasingly relevant as pre-trained models grow in scale and complexity [12]. Group 3: Terminology and Framework - The article identifies a terminological confusion in the research community, where similar adaptation methods are referred to differently across fields, such as "prompt tuning" in NLP and "model reprogramming" in machine learning literature [14]. - Despite the different names, these methods fundamentally leverage the same property of neural networks—reprogrammability—leading to the proposal of a unified framework that connects these disparate research areas [14][17]. Group 4: Mathematical Expression of Reprogrammability - The article provides a mathematical framework for neural network reprogrammability, defining how a fixed pre-trained model can be adapted to new tasks through configurable transformations without changing the model's parameters [25][34]. Group 5: Case Studies of Reprogrammability - The article illustrates three methods of reprogramming using a vision-language model, highlighting how each method achieves the same goal of reusing a frozen model for new tasks through different computational paths [27][30]. - Input manipulation and output alignment are key components of these methods, allowing for effective task adaptation without additional training parameters [30][32].