Worked on stabilizing machine learning metrics collection in the volcengine/verl repository by addressing a logging issue related to MLFlow metric names. Implemented a data validation solution in Python that sanitizes metric names, replacing invalid characters to prevent logging errors across different environments. Enhanced the reliability of metrics dashboards by adding unit tests to verify the sanitization logic, ensuring robust metric logging and compatibility throughout ML pipelines. Focused on improving operational workflows for ML experiments, this work emphasized careful logging practices and thorough unit testing, resulting in more stable metric collection and reduced friction for cross-environment reporting and dashboard generation.