Developed and integrated the ConditionalUpdateTransformation feature in the apple/axlearn repository to enhance flexibility in GAN training workflows. This solution enabled scheduled pause and resume of learning, as well as alternating training across multiple model branches, supporting more robust experimentation and scalable model development. The implementation leveraged Python and JAX, applying deep learning and testing expertise to coordinate training-time transformations with ongoing codebase changes. By minimizing integration overhead, the work facilitated faster iteration and improved alignment with project goals. The approach demonstrated thoughtful application of design patterns for flexible training loops, contributing to more efficient and adaptable machine learning pipelines.