MLE-Bench
A competition-style benchmark for machine learning engineering tasks that evaluates the performance of AI systems on autonomous modeling tasks using medal rate as the primary metric.
Timeline (last 90 days)
MLE-Bench was proposed by OpenAI, is collected from real Kaggle competitions, and requires agents to complete the full pipeline of data loading, feature engineering, model training and prediction submission within a limited time.
Medal rate refers to the proportion of agent submissions that reach the bronze medal threshold of that competition or above, directly benchmarked against the relative performance of human contestants.
AIBuildAI-2.5 ranked first on MLE-Bench, achieving a medal rate of 73.3%.
MLE-Bench is a standardized evaluation framework proposed by OpenAI that adapts historical Kaggle competition tasks into an automatically scorable benchmark with gold, silver, and bronze medal tiers.
All Facts (3)
MLE-Bench was proposed by OpenAI, is collected from real Kaggle competitions, and requires agents to complete the full pipeline of data loading, feature engineering, model training and prediction submission within a limited time.
50%UnverifiedMedal rate refers to the proportion of agent submissions that reach the bronze medal threshold of that competition or above, directly benchmarked against the relative performance of human contestants.
50%UnverifiedMLE-Bench is a standardized evaluation framework proposed by OpenAI that adapts historical Kaggle competition tasks into an automatically scorable benchmark with gold, silver, and bronze medal tiers.
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