Adversarial examples have become a critical focus in ensuring the security and robustness of deep learning (DL) systems. In this paper, we introduce an innovative approach for generating adversarial examples, designed to identify and diagnose common errors in DL models. Specifically, our method targets two key issues: Oscillating Loss (OL) and Slow Convergence (SC), providing valuable insight into model performance and fault detection. Using a reinforcement learning framework, we generate test data that effectively distinguishes between models with and without these errors. We consider the MNIST and CIFAR-10 datasets and test our approach on neural networks with various architectures, demonstrating significant improvements in error detection across different types of models. These results highlight the substantial effectiveness of our proposed method in improving the reliability of DL models. Furthermore, we demonstrate the scalability of our approach, showing that it can be used to diagnose various common errors in DL models with minimal modifications.