From Linear to Nonlinear: Provable Weak-to-Strong Generalization through Feature Learning

A recent paper on arXiv explores the concept of weak-to-strong generalization, where a stronger model trained under the guidance of a weaker one can achieve better performance. This research provides a formal analysis of this phenomenon, moving beyond previous studies that were often limited to abstract or linear models. By examining the transition from a linear CNN to a two-layer ReLU CNN, the authors shed light on how feature learning can enhance model capabilities. This work is significant as it deepens our understanding of model training and could lead to more effective machine learning strategies.