Worked on the pytorch/torchtitan repository to enhance observability and stability in distributed training environments. Addressed a critical issue by restoring trainer logging functionality after distributed initialization, which had been disrupted by missing job_config.maybe_log() calls. This fix, implemented in Python and validated across distributed setups, ensured that logging remained consistent and reliable for debugging and monitoring purposes. The work focused on backend development and distributed systems, reducing the time required to diagnose distributed initialization issues and improving trust in run metrics. By resolving this bug, the developer contributed to more effective monitoring and debuggability in complex distributed training workflows.