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MLE-bench for Engineering Tasks
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This research introduces MLE-bench, a benchmark for evaluating how well AI agents perform machine learning engineering tasks. The benchmark is comprised of 75 Kaggle competitions, chosen for their difficulty and representativeness of real-world ML engineering skills. Researchers evaluated several state-of-the-art language models on MLE-bench, finding that the best-performing model achieved at least a bronze medal in 16.9% of the competitions. The researchers also explored how performance varies based on factors like the amount of time and compute resources available to the agents. Finally, the paper discusses potential issues like contamination (when agents learn from publicly available solutions), as well as the limitations of MLE-bench. The goal is to understand the capabilities and risks associated with AI agents that can autonomously perform machine learning engineering.
Read more: https://arxiv.org/pdf/2410.07095v1
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