Worked on the yhyang201/sglang repository to optimize the reranking process for large datasets by introducing a more efficient top-N retrieval method. The solution replaced a full list sort with Python’s heapq.nlargest, reducing both computation and memory overhead in the ranking pipeline. This targeted feature improved latency and throughput, particularly in backend data processing scenarios where performance is critical. The work demonstrated a focused approach to algorithm optimization, emphasizing concise and maintainable code changes. No bug fixes were addressed during this period, as the primary objective was to enhance performance using Python’s standard library and backend development best practices.