Evals & Safety
MLE-bench
An OpenAI benchmark that tests AI agents on machine learning engineering, using 75 real Kaggle competitions that require preparing data, training models, and running experiments.
In depth
An OpenAI benchmark that tests AI agents on machine learning engineering, using 75 real Kaggle competitions that require preparing data, training models, and running experiments. Each submission is graded against that competition's human leaderboard, so results read as Kaggle medals. In the original paper the best setup, o1-preview with the AIDE scaffold, reached at least bronze-medal level in 16.9% of competitions. It is often cited in discussions of AI automating AI research.
Primary source: arxiv.org
Example
In practice, developers reach for MLE-bench when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Evals & Safety.
FAQ
Common questions
What is MLE-bench?
An OpenAI benchmark that tests AI agents on machine learning engineering, using 75 real Kaggle competitions that require preparing data, training models, and running experiments.
Why does MLE-bench matter for AI developers?
MLE-bench sits in the Evals & Safety part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about MLE-bench?
Developers Digest publishes tutorials and videos that cover Evals & Safety topics including MLE-bench. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Evals & Safety
MATH Benchmark
A dataset of 12,500 competition mathematics problems, each with a full step-by-step solution, released by Dan Hendrycks and colleagues in 2021.
Evals & Safety
HumanEval
A code generation benchmark from OpenAI made of 164 hand-written Python programming problems, each with a function signature, a docstring, and unit tests.
Evals & Safety
TruthfulQA
A benchmark of 817 questions across 38 categories, including health, law, finance, and politics, written so that a common human misconception produces a confident wrong answer.
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