Worked on stabilizing the core training workflow in the Liger-Kernel repository by addressing a critical bug in the LigerFusedLinearCrossEntropyFunction. Focused on improving code reliability, the developer modified the gradient-saving logic to guard on grad_bias rather than bias, preventing an AttributeError when grad_bias is None. This adjustment allowed training to continue smoothly in scenarios where gradients are not required, reducing interruptions during experimentation. The solution was implemented in Python and validated through CPU-only tests and style checks, ensuring robust integration. Leveraging deep learning and machine learning expertise, the work enhanced training stability without altering kernel performance or introducing new features.