MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine
  Learning Engineering
Authors: name: Rushi Qiang, name: Yuchen Zhuang, name: Yinghao Li, name: Dingu Sagar V K, name: Rongzhi Zhang, name: Changhao Li, name: Ian Shu-Hei Wong, name: Sherry Yang, name: Percy Liang, name: Chao Zhang, name: Bo Dai

We introduce MLE-Dojo, a Gym-style framework for systematically reinforcement
learning, evaluating, and improving autonomous large language model (LLM)
agents in iterative machine learning engineering (MLE) workflows. 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. Built upon 200+ real-world Kaggle challenges, 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. 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. Extensive evaluations of eight frontier LLMs reveal that
while current models achieve meaningful iterative improvements, they still
exhibit significant limitations in autonomously generating long-horizon
solutions and efficiently resolving complex errors. Furthermore, MLE-Dojo's
flexible and extensible architecture seamlessly integrates diverse data
sources, tools, and evaluation protocols, uniquely enabling model-based agent
tuning and promoting interoperability, scalability, and reproducibility. We
open-source our framework and benchmarks to foster community-driven innovation
towards next-generation MLE agents.