Worked on improving elastic distributed training reliability in the pytorch/pytorch repository by addressing the stability of the rendezvous shutdown process. Focused on ensuring that the rendezvous mechanism only shuts down when an entire training run completes or fails, rather than when a single worker departs. This adjustment preserves the integrity of large-scale distributed training sessions and reduces unnecessary interruptions. Utilized Python programming skills and knowledge of distributed systems to implement the fix, specifically targeting elastic training frameworks. The work involved debugging and modifying core coordination logic, resulting in more robust handling of worker participation and session lifecycle within distributed training environments.