AIDE: AI-Driven Exploration in the Space of Code
Machine learning, the foundation of modern artificial intelligence, has
driven innovations that have fundamentally transformed the world. Yet, behind
advancements lies a complex and often tedious process requiring labor and
compute intensive iteration and experimentation. Engineers and scientists
developing machine learning models spend much of their time on trial-and-error
tasks instead of conceptualizing innovative solutions or research hypotheses.
To address this challenge, we introduce AI-Driven Exploration (AIDE), a machine
learning engineering agent powered by large language models (LLMs). AIDE frames
machine learning engineering as a code optimization problem, and formulates
trial-and-error as a tree search in the space of potential solutions. By
strategically reusing and refining promising solutions, AIDE effectively trades
computational resources for enhanced performance, achieving state-of-the-art
results on multiple machine learning engineering benchmarks, including our
Kaggle evaluations, OpenAI MLE-Bench and METRs RE-Bench.
67b6e0829b29983879ad2312
Zhengyao Jiang
admin_assigned
2025-02-20T17:38:24.557Z
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Zhengyao Jiang
user
ZhengyaoJiang
67b6e0829b29983879ad2313
Dominik Schmidt
admin_assigned
2025-02-20T17:38:52.544Z
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Dominik Schmidt
user
dominikschmidt
67b6e0829b29983879ad2314
Dhruv Srikanth
admin_assigned
2025-02-20T17:38:58.578Z
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Dhruv Srikanth
user
dSrikanth
67b6e0829b29983879ad2315
Dixing Xu
claimed_verified
2025-02-20T15:52:52.450Z
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Dex Dixing Xu
user
dexhunter
67b6e0829b29983879ad2316
Ian Kaplan
67b6e0829b29983879ad2317
Deniss Jacenko
claimed_verified
2025-03-25T08:21:46.526Z
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Deniss Jacenko
user
jacenko
67b6e0829b29983879ad2318
Yuxiang Wu
AIDE: AI-Driven Exploration in the Space of Code
Machine learning, the foundation of modern artificial intelligence, has
driven innovations that have fundamentally transformed the world. Yet, behind
advancements lies a complex and often tedious process requiring labor and
compute intensive iteration and experimentation. Engineers and scientists
developing machine learning models spend much of their time on trial-and-error
tasks instead of conceptualizing innovative solutions or research hypotheses.
To address this challenge, we introduce AI-Driven Exploration (AIDE), a machine
learning engineering agent powered by large language models (LLMs). AIDE frames
machine learning engineering as a code optimization problem, and formulates
trial-and-error as a tree search in the space of potential solutions. By
strategically reusing and refining promising solutions, AIDE effectively trades
computational resources for enhanced performance, achieving state-of-the-art
results on multiple machine learning engineering benchmarks, including our
Kaggle evaluations, OpenAI MLE-Bench and METRs RE-Bench.
AI-Driven Exploration, utilizing large language models, optimizes machine learning engineering tasks through code optimization and strategic solution reuse, achieving top results on various benchmarks.
large language models machine learning engineering agent code optimization tree search machine learning engineering benchmarks Kaggle evaluations OpenAI MLE-Bench METRs RE-Bench