During March 2026, Ziheng Wang focused on improving reward calculation integrity in the alibaba/ROLL repository. He addressed a bug in the MultipleChoiceBoxedRuleRewardWorker, where rewards could incorrectly evaluate to zero due to improper initialization. By initializing response_level_rewards directly from the scores tensor, he ensured accurate reward assignment and prevented downstream analytics errors. This targeted hotfix, implemented in Python and leveraging tensor-based debugging, required precise code edits with minimal impact on the broader codebase. Ziheng’s work demonstrated depth in machine learning and reinforcement learning, delivering a robust solution that enhanced both user trust and the reliability of business incentives.