Faster sorting algorithms discovered using deep reinforcement learning

Daniel J. Mankowitz, Andrea Michi, Anton Zhernov, Marco Gelmi, Marco Selvi, Cosmin Păduraru, Edouard Leurent, Shariq Iqbal et al.

Nature 2023 article cited by 175

importance 8/10 solved Evolutionary Algorithms and Applications software infrastructure stage: deployed

Improving the efficiency of fundamental sorting routines had stalled for both human scientists and computational approaches.

Sorting runs trillions of times daily, so faster routines save computation at global scale.

AlphaDev, a deep reinforcement learning agent trained on a sorting-optimization game, discovered new small sorting algorithms from scratch that beat human benchmarks.

Discovered sorting algorithms outperforming previously known human benchmarks; integrated into the LLVM standard C++ sort library

Discovered algorithms were adopted into LLVM, a widely used C++ library, indicating real-world deployment.

Venue Nature
Published 2023
Work type article
Authors Daniel J. Mankowitz, Andrea Michi, Anton Zhernov, Marco Gelmi, Marco Selvi, Cosmin Păduraru, Edouard Leurent, Shariq Iqbal et al.
Citations 175 (OpenAlex, at import time)
References 64
Model or method AlphaDev (deep reinforcement learning agent)
Organization not stated in source
Primary source https://doi.org/10.1038/s41586-023-06004-9
Open access hybrid
Keywords reinforcement learning, sorting algorithms, AlphaDev, LLVM, program optimization
8 / 10

major — millions of lives, an industry or a field

Abstract states fundamental algorithms run trillions of times daily; discovered faster sorting routines integrated into LLVM, a widely used C++ library, affecting global computation.

Basis: stated. The source itself states the scale. Stated scale: sorting/hashing used trillions of times on any given day. Method and limits.

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