Yangky worked on the sgl-project/sglang repository, focusing on improving the reliability of distributed training workflows. During the month, Yangky addressed a bug in the Distributed Training Tokenizer Manager by correcting an assertion message in Python, ensuring that the runtime error accurately reflected the requirement for dp_size to be one when updating weights from a distributed source. This targeted code refactoring enhanced the maintainability and observability of the codebase, reducing potential confusion for developers debugging distributed weight updates. The fix was carefully scoped to minimize regression risk, demonstrating a thoughtful approach to stability and clarity in distributed machine learning systems.