MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering (2505.07782v1)

Abstract: We introduce MLE-Dojo, a Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous 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.

Overview of MLE-Dojo Framework

The paper introduces MLE-Dojo, a comprehensive framework designed to empower and evaluate LLM agents in machine learning engineering (MLE) workflows. The primary innovation of MLE-Dojo is its interactive environment, which contrasts with traditional benchmarks by enabling iterative experimentation, debugging, and solution refinement through structured feedback loops. This approach offers a departure from static datasets or single-attempt evaluations, positioning MLE-Dojo as a versatile platform for developing autonomous LLM agents.

Key Features and Methodological Insights

MLE-Dojo is built upon over 200 real-world Kaggle competitions, which encompass diverse MLE tasks such as data processing, architecture search, hyperparameter tuning, and code debugging. The framework facilitates comprehensive agent training via both supervised fine-tuning and reinforcement learning, thereby supporting iterative experimentation and real-time outcome verification. MLE-Dojo’s architecture is highly flexible and extensible, allowing seamless integration with diverse data sources, tools, and evaluation protocols. This promotes model interoperability, scalability, and reproducibility—features crucial for advancing autonomous ML agent technologies.

The paper evaluates eight leading LLMs within MLE-Dojo's interactive environment, revealing that despite achieving iterative improvements, current models face challenges in autonomously generating long-horizon solutions and resolving complex errors. MLE-Dojo’s evaluations point towards the limitations inherent in present-day LLMs, underscoring the need for enhanced capabilities in long-context reasoning and autonomous task execution.

Numerical Results and Analytical Observation

The extensive evaluations conducted in the paper show that current LLMs exhibit meaningful iterative improvements in solving engineering tasks. However, they also highlight significant limitations, particularly in dealing with long-horizon autonomy and complex error resolution. The benchmark reveals instances where models effectively surpass human competitors in specific tasks but also identify areas where models falter, providing critical feedback for training regimes.

MLE-Dojo implements the HumanRank score as a feedback mechanism for quantifying agent performance relative to human benchmarks, offering normalized and consistent performance metrics across diverse task scenarios. The paper adopts the Elo rating system to assess pairwise model performance, facilitating an understanding of competitive dynamics and further elucidating areas for model improvement.

Implications and Future Directions

The implications of MLE-Dojo are manifold. Practically, it offers a robust environment for assessing LLM agents' potential in automating complex MLE workflows and highlights the current limitations of these models, necessitating iterative refinement in training strategies and architectural developments. Theoretically, MLE-Dojo contributes to understanding the scalability and adaptability of LLMs in real-world MLE workloads, fostering insights into their application ecosystems.

Future developments in AI may focus on enhancing LLM architectures to address currently identified deficiencies in long-term reasoning capabilities and error correction processes. The MLE-Dojo framework, through its open-source release, catalyzes community-driven innovation, potentially leading to the emergence of next-generation MLE agents with improved efficiency, reliability, and scalability.

Conclusion

MLE-Dojo stands as a pioneering framework in the field of machine learning engineering, offering a rich, interactive platform for empowering and evaluating LLM agents. Its emphasis on iterative feedback and solution refinement marks a significant progression in understanding and developing autonomous MLE technologies. By leveraging MLE-Dojo, researchers and practitioners alike can contribute to advancing autonomous agent capabilities, paving the way for more sophisticated and effective machine learning solutions in various domains.