MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline

• URL: http://arxiv.org/abs/2510.07307v1
• Date: Wed, 08 Oct 2025 17:57:19 GMT
• Title: MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline
• Authors: Rushi Qiang, Yuchen Zhuang, Anikait Singh, Percy Liang, Chao Zhang, Sherry Yang, Bo Dai,
• Abstract summary: We introduce MLE-Smith, a fully automated multi-agent pipeline to transform raw datasets into competition-style MLE challenges.<n>MLE-Smith drives structured task design and standardized, coupled with a hybrid verification mechanism that enforces strict structural rules and high-level semantic soundness.<n>We generate 606 tasks spanning multiple categories, objectives, and modalities, demonstrating that MLE-Smith can work effectively across a wide range of real-world datasets.
• Score: 60.80254167794536
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: While Language Models (LMs) have made significant progress in automating machine learning engineering (MLE), the acquisition of high-quality MLE training data is significantly constrained. Current MLE benchmarks suffer from low scalability and limited applicability because they rely on static, manually curated tasks, demanding extensive time and manual effort to produce. We introduce MLE-Smith, a fully automated multi-agent pipeline, to transform raw datasets into competition-style MLE challenges through an efficient generate-verify-execute paradigm for scaling MLE tasks with verifiable quality, real-world usability, and rich diversity. The proposed multi-agent pipeline in MLE-Smith drives structured task design and standardized refactoring, coupled with a hybrid verification mechanism that enforces strict structural rules and high-level semantic soundness. It further validates empirical solvability and real-world fidelity through interactive execution. We apply MLE-Smith to 224 of real-world datasets and generate 606 tasks spanning multiple categories, objectives, and modalities, demonstrating that MLE-Smith can work effectively across a wide range of real-world datasets. Evaluation on the generated tasks shows that the performance of eight mainstream and cutting-edge LLMs on MLE-Smith tasks is strongly correlated with their performance on carefully human-designed tasks, highlighting the effectiveness of the MLE-Smith to scaling up MLE tasks, while maintaining task quality.

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