The insights gained from the theta-lazy regime can be instrumental in enhancing the generalization performance of overparameterized neural networks. By understanding the critical role of the initial scale κ in driving neural networks to zero training loss rapidly, researchers and practitioners can leverage this knowledge to optimize the initialization schemes for neural networks. Ensuring that the initial scale surpasses a certain threshold can lead to faster convergence and improved generalization capabilities. Additionally, the identification of distinct initialization regimes, such as the theta-lazy area, provides a framework for fine-tuning the training dynamics to achieve better generalization performance. By incorporating the principles of the theta-lazy regime into the design and training of neural networks, it is possible to enhance their ability to generalize well to unseen data.