Ali Hkwala developed a CartPole Checkpoint Visualization feature for the kscalelabs/ksim repository, focusing on enhancing experiment observability and reproducibility in reinforcement learning workflows. Using Python, environment visualization, and model loading techniques, Ali implemented an end-to-end workflow that allows users to evaluate CartPole episodes with either the latest checkpointed policy or a randomly initialized policy. The system visualizes each episode, logs rewards, and introduces controlled pauses to facilitate observation and debugging. This work addressed the need for faster policy iteration and more transparent experiment tracking, laying a foundation for broader checkpoint-based evaluation in reinforcement learning research and development.