Worked on the liguodongiot/transformers repository to improve the correctness and efficiency of the load-balancing loss function used in expert selection. Addressed a critical bug by removing a redundant double soft-max operation, which enhanced both computational efficiency and the accuracy of expert routing. The solution focused on stability and maintainability, ensuring that the model’s performance was not compromised by unnecessary computations. Utilized Python and PyTorch, applying deep learning and model optimization techniques to refine the loss function. No new features were introduced during this period, as the primary goal was to resolve existing issues and strengthen the codebase’s reliability.