Worked on the google/flax repository to enhance the stability of Variational Autoencoder (VAE) training by addressing a critical bug in the training loop. The solution involved correcting the invocation of optimizer.update, ensuring the correct number of arguments and removing the dependency on the model object for gradient processing. This adjustment improved the robustness and reproducibility of VAE experiments, reducing runtime errors and minimizing training interruptions. The work demonstrated strong proficiency in Python, JAX, and deep learning workflows, with a focus on backend reliability rather than new features, ultimately enabling faster iteration and more consistent results for machine learning research.