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MLE-bench
🤖 MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
The paper introduces MLE-bench, a benchmark designed to evaluate AI agents' ability to perform machine learning engineering tasks. The benchmark comprises 75 Kaggle competitions, each requiring agents to solve real-world problems involving data preparation, model training, and code debugging. Researchers evaluated several cutting-edge language models on MLE-bench, with the best-performing setup achieving at least a bronze medal in 16.9% of the competitions. The paper investigates various factors influencing performance, such as resource scaling and contamination from pre-training, and concludes that while current agents demonstrate promising capabilities, significant challenges remain.
📎 Link to paper
The paper introduces MLE-bench, a benchmark designed to evaluate AI agents' ability to perform machine learning engineering tasks. The benchmark comprises 75 Kaggle competitions, each requiring agents to solve real-world problems involving data preparation, model training, and code debugging. Researchers evaluated several cutting-edge language models on MLE-bench, with the best-performing setup achieving at least a bronze medal in 16.9% of the competitions. The paper investigates various factors influencing performance, such as resource scaling and contamination from pre-training, and concludes that while current agents demonstrate promising capabilities, significant challenges remain.
📎 Link to paper
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By Shahriar Shariati🤖 MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
The paper introduces MLE-bench, a benchmark designed to evaluate AI agents' ability to perform machine learning engineering tasks. The benchmark comprises 75 Kaggle competitions, each requiring agents to solve real-world problems involving data preparation, model training, and code debugging. Researchers evaluated several cutting-edge language models on MLE-bench, with the best-performing setup achieving at least a bronze medal in 16.9% of the competitions. The paper investigates various factors influencing performance, such as resource scaling and contamination from pre-training, and concludes that while current agents demonstrate promising capabilities, significant challenges remain.
📎 Link to paper
The paper introduces MLE-bench, a benchmark designed to evaluate AI agents' ability to perform machine learning engineering tasks. The benchmark comprises 75 Kaggle competitions, each requiring agents to solve real-world problems involving data preparation, model training, and code debugging. Researchers evaluated several cutting-edge language models on MLE-bench, with the best-performing setup achieving at least a bronze medal in 16.9% of the competitions. The paper investigates various factors influencing performance, such as resource scaling and contamination from pre-training, and concludes that while current agents demonstrate promising capabilities, significant challenges remain.
📎 Link to paper