Worked on the sgl-project/sglang repository, focusing on improving the reliability of distributed training workflows. Addressed a bug in the Distributed Training Tokenizer Manager by correcting an assertion message in Python to ensure that runtime errors accurately reflect the condition where dp_size must be 1 during distributed weight updates. This targeted code refactoring enhanced the maintainability and observability of the codebase, making debugging more straightforward for developers working with distributed systems. The fix was implemented with minimal regression risk, aligning error messaging with the actual implementation logic and supporting stable, predictable behavior in distributed training environments using Python.