Worked on the deepspeedai/DeepSpeed repository to address a critical training instability in the DeepSpeed Zero2 engine. Focused on improving reliability, the developer identified and fixed a synchronization issue between reduction and current CUDA streams during double ipg_buffer swapping, which previously led to premature zero loss during large-scale training runs. The solution involved targeted debugging, code review, and enhancements to test coverage for the Zero2 path. Utilizing Python and deep learning expertise, along with distributed systems knowledge, the work restored stable loss signaling and improved convergence behavior, ultimately reducing wasted compute and enabling more predictable performance for Zero2 users.