Developed and delivered the CartPole Checkpoint Visualization feature for the kscalelabs/ksim repository, establishing an end-to-end workflow for evaluating reinforcement learning policies. Leveraging Python and skills in environment visualization and model loading, the implementation enabled users to run CartPole episodes using either the latest checkpointed policy or a randomly initialized policy. The workflow visualized each episode, logged rewards, and introduced controlled pauses to facilitate observation and debugging. This approach improved experiment observability, reproducibility, and accelerated policy iteration. The work laid a foundation for broader checkpoint-based evaluation within ksim, supporting more reliable and efficient reinforcement learning experimentation and development.