Researchers introduced AlphaDev, a deep reinforcement learning agent, that discovered faster sorting algorithms by framing the problem as a game played with CPU instructions. This AI agent outperformed existing human-developed benchmarks for small sorting routines, leading to their integration into the LLVM standard C++ sort library, a widely used component. AlphaDev achieved these improvements by optimizing for actual measured latency at the CPU instruction level, even finding novel instruction sequences called "swap move" and "copy move." The study also demonstrated AlphaDev's potential to generalize to other algorithm optimization challenges beyond sorting, such as protocol buffer deserialization, suggesting a new approach to fundamental algorithm discovery.Source: https://www.nature.com/articles/s41586-023-06004-9