Worked on the allenai/open-instruct repository to enhance the robustness of the PolicyTrainerRayProcess training loop. Addressed a critical issue in data processing by ensuring that only complete batches are processed during deep learning model training, thereby preventing data leakage from leftover points. Utilized Python to implement a math.ceil-based calculation for batch accumulation and explicitly dropped any data points that did not form a full batch. This approach improved the accuracy and reproducibility of reinforcement learning experiments by aligning the training process with full-batch guarantees, ultimately contributing to more reliable model updates and better outcomes for downstream machine learning tasks.