Reducing runtime in competitive programming is all about choosing the most efficient algorithm for the task, minimizing redundant calculations, and using data structures that offer fast access times. Starting with analyzing the time complexity of different algorithms is crucial; opt for algorithms with the lowest complexity that still meet the problem's requirements. Pre-computing results for repetitive operations, using techniques like memoization or dynamic programming, also helps save time. Simplifying loops and avoiding unnecessary iterations further contributes to performance gains. Additionally, opting for optimized data structures like hash maps for lookups or priority queues for sorting tasks can dramatically improve speed. By combining these techniques, competitive programmers can create solutions that are both correct and perform efficiently within the constraints, making runtime optimization an essential skill.