During August 2025, this developer focused on improving the Mixture of Experts (MoE) implementation in the huggingface/torchtitan repository. They addressed a subtle issue with bias updates, eliminating double-counting during recomputation and refining the logic to ensure correctness. By optimizing how expert usage is tracked, they reduced unnecessary computational overhead, which led to more efficient training and better resource utilization. Their work, implemented and documented in Python using PyTorch, enhanced the stability and reproducibility of large-scale deep learning experiments. This contribution demonstrated a strong grasp of algorithm optimization and machine learning, delivering targeted improvements to a complex training pipeline.