During October 2025, Gyin focused on backend development and debugging for the ROCm/pytorch repository, addressing stability issues in CUDA workflows. Gyin reverted a previous change in functionalization and proxy tensor modes, restoring the original decomposition behavior to resolve CUDA OutOfMemory errors that had emerged. This involved targeted code reversion, careful adjustment of dispatch key sets, and pruning of specific operations from decomposition lists to match the prior, stable state. Working primarily in Python and C++, Gyin collaborated with autograd and functionalization teams, demonstrating depth in PyTorch internals and ensuring more reliable training performance for CUDA workloads in production environments.