Worked on stabilizing KLDivLoss gradient computation for the Ascend NPU within the Liger-Kernel repository, focusing on aligning NPU results with those from the Triton kernel. Addressed a persistent issue by implementing a custom math-based gradient path in Python, replacing the native operator to ensure correctness when beta is not zero. Enhanced test coverage and reliability by updating and validating gradient tests, particularly for production-scale deep learning and machine learning workflows. Emphasized code quality and maintainability through standard testing and style checks, contributing to more robust NPU development and improved inference and training stability using PyTorch.