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

By Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, E. Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan and 2 more. arXiv.org, 2024

302 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/7c44b7fdcec2e517799f6c54f6ba42bf1a89d2e6

Citations in the corpus

• A benchmark of expert-level academic questions to assess AI capabilities (2025, unknown): “Weaknesses: -”
• Large Language Models for Information Retrieval: A Survey (2023, unknown): “In the context of retrieval, LLMs have improved retrieval accuracy by enabling more nuanced matching between queries and documents, considering context as well.”