用AI自动探索代码优化路径，加速机器学习工程迭代。
AIDE: AI-Driven Exploration in the Space of Code
- 将编码调试转化为基于树搜索的智能优化过程
- 在多个基准测试中达到顶尖性能，包括Kaggle和OpenAI MLE-Bench
- 适合希望减少重复实验、提升研发效率的工程师
机器学习作为现代人工智能的核心，推动了世界性变革。然而其背后依赖复杂且耗时的反复试验与迭代，导致工程师和科学家大量时间耗费于试错而非创新。为此，我们提出人工智能驱动的探索框架（AIDE），一个基于大语言模型（LLMs）的机器学习工程代理。AIDE将机器学习工程视为代码优化问题，把试错过程建模为潜在解决方案空间中的树搜索。通过有策略地复用和优化已有方案，有效以计算资源换取性能提升，在多个机器学习工程基准上取得领先结果，包括我们自有的Kaggle评估、OpenAI MLE-Bench和METRs RE-Bench。
原文摘要 · Abstract (English)
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.
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