Worked on stabilizing distributed checkpointing in the huggingface/torchtitan repository by addressing a PyTorch distributed checkpoint loading bug. Developed a targeted workaround in Python that ensures stateful objects are accurately preserved during checkpoint and load cycles, which is essential for reliable model recovery in multi-node deep learning training. This solution reduced the risk of state drift and data loss, directly improving the stability of distributed training workflows. The approach was closely aligned with ongoing upstream efforts in the PyTorch community, demonstrating a collaborative and detail-oriented engineering process focused on robust machine learning infrastructure and production-grade software development using PyTorch.