In February 2026, Koushu Rui focused on optimizing memory usage in the modelscope/data-juicer repository by refactoring the convert_to_absolute_paths function. By introducing generator-based processing in Python, Koushu enabled more efficient handling of data samples, reducing peak memory consumption and improving throughput for large datasets. The technical approach centered on memory profiling and performance-oriented refactoring, targeting scalability and resource efficiency in data processing pipelines. Although no bugs were fixed during this period, the work demonstrated depth in Python optimization and data processing, resulting in a more scalable and cost-effective solution for handling large-scale data within the repository’s processing workflows.