Recyclable Tuning

Recyclable tuning focuses on efficiently adapting pre-trained models to new tasks by reusing previously learned parameters, rather than discarding them after each adaptation. Current research explores methods like initialization-based and distillation-based approaches to leverage these "outdated" weights, improving both training speed and performance, particularly in continual pre-training scenarios. This research aims to reduce computational costs and resource waste associated with repeatedly training large models, impacting both the efficiency of model development and the sustainability of AI research. Furthermore, techniques like "top-tuning," which only trains a smaller classifier on top of pre-trained features, offer significant speed improvements for specific tasks.