In January 2025, Yaocheng Ji addressed a critical issue in the pytorch/xla repository by fixing BatchNorm behavior under automatic mixed precision (AMP) on TPUs and GPUs. He ensured that lower-precision inputs such as FP16 and BF16 are correctly processed when BatchNorm weights remain in FP32, resolving incorrect normalization results during mixed-precision training. His approach included adding a regression test to validate the fix and prevent future issues. Working primarily in C++ and Python, Yaocheng applied deep learning expertise and performance optimization skills, delivering a targeted solution that improved stability and reliability for mixed-precision workflows on XLA backends.