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title: README |
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paper: https://arxiv.org/abs/2505.07782 |
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MLE-Dojo is a Gym-style framework for systematically training, evaluating, and improving autonomous large language model (LLM) agents in iterative machine learning engineering (MLE) workflows. |
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Unlike existing benchmarks that primarily rely on static datasets or single-attempt evaluations, MLE-Dojo provides an interactive environment enabling agents to iteratively experiment, debug, and refine solutions through structured feedback loops. |
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Built upon 200+ real-world Kaggle challenges (e.g., tabular data analysis, computer vision, natural language processing, and time series forecasting), MLE-Dojo covers diverse, open-ended MLE tasks carefully curated to reflect realistic engineering scenarios such as data processing, architecture search, hyperparameter tuning, and code debugging. |
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Its fully executable environment supports comprehensive agent training via both supervised fine-tuning and reinforcement learning, facilitating iterative experimentation, realistic data sampling, and real-time outcome verification. |
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📄 **Check out our paper:** [MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering](https://arxiv.org/abs/2505.07782) |
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