Over a two-month period, this developer contributed to modelscope/ms-swift and alibaba/ChatLearn, focusing on optimizing training and inference workflows in distributed machine learning systems. They implemented dynamic bucketing for persistent cache padding and introduced a flattened data collator, both aimed at improving memory efficiency and throughput using Python. In alibaba/ChatLearn, they enhanced distributed training by sorting samples within global batches for better data-parallel balance and added a skip-generation mode to accelerate reproducibility and iteration. Their work emphasized cache management, data loading optimization, and performance tuning, resulting in smoother evaluation runs and reduced overhead in large-scale model training environments.