{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mle-bench-evaluating-machine-learning-agents","title":"MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering","arxiv_id":"2410.07095","date":"2024-10-09","proceeding":null,"authors":["Jun Shern Chan","Neil Chowdhury","Oliver Jaffe","James Aung","Dane Sherburn","Evan Mays","Giulio Starace","Kevin Liu","Leon Maksin","Tejal Patwardhan","Lilian Weng","Aleksander Mądry"],"abstract":"We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. 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