MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

• URL: http://arxiv.org/abs/2410.07095v2
• Date: Thu, 24 Oct 2024 12:35:50 GMT
• Title: MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
• 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 summary: MLE-bench is a benchmark for measuring how well AI agents perform at machine learning engineering. We curate 75 ML engineering-related competitions from Kaggle. We establish human baselines for each competition using Kaggle's publicly available leaderboards.
• Score: 35.237253622981264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. To this end, we curate 75 ML engineering-related competitions from Kaggle, creating a diverse set of challenging tasks that test real-world ML engineering skills such as training models, preparing datasets, and running experiments. We establish human baselines for each competition using Kaggle's publicly available leaderboards. We use open-source agent scaffolds to evaluate several frontier language models on our benchmark, finding that the best-performing setup--OpenAI's o1-preview with AIDE scaffolding--achieves at least the level of a Kaggle bronze medal in 16.9% of competitions. In addition to our main results, we investigate various forms of resource scaling for AI agents and the impact of contamination from pre-training. We open-source our benchmark code (github.com/openai/mle-bench/) to facilitate future research in understanding the ML engineering capabilities of AI agents.

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