During May 2025, Chengdong Wang developed the Self-Play Fine-Tuning (SPIN) algorithm for the volcengine/verl repository, focusing on reinforcement learning for large language models. He adapted the existing PPO framework to a DPO-based objective, replacing the critic with a reference model and shifting the update signal to log-probability differences. This required reworking data handling to support preference pairs, enabling stable self-play fine-tuning. Using Python and PyTorch, Chengdong established a foundation for improved sample efficiency and policy alignment. His work demonstrated depth in distributed systems and model fine-tuning, enabling faster experimentation and supporting stronger business value for Verl.