Worked on the volcengine/verl repository to deliver V2 rollout enhancements focused on multi-step inference persistence and improved reproducibility in reinforcement learning workflows. Developed features in Python that extended skip_rollout capabilities from V1 and enabled saving and loading of multi-step inference data, allowing reliable replay of training runs. Addressed long-standing issues related to V1 rollout and resolved the unmerged new_batch problem, ensuring full replay of dumped results. Integrated updates across core components, including trainer and rollout configuration, to support deterministic, config-driven training. Leveraged skills in data management and machine learning to improve stability, throughput, and traceability across experiments.