MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering
35B MLExpert raises MLE-Bench Lite medal rate from 37.9% to 72.7%.
The paper introduces a post-training method for autonomous, long-horizon machine-learning engineering and uses it to train MLExpert, a 35B Mixture-of-Experts agentic model. Hierarchical agentic supervised fine-tuning and Windowed Interaction RL learn from intermediate experiment outcomes, with asynchronous collection enabling updates before a trajectory ends. On MLE-Bench Lite, MLExpert raises the base model’s average medal rate from 37.9% to 72.7%, comparable to larger models such as Kimi-K3 and GLM-5.3. It also improves NatureBench Lite, agent-scaffold optimization, and algorithm optimization.
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