MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering
Teaching AI to autonomously design and improve machine learning systems
Researchers trained MLExpert, a specialized AI model, to autonomously run machine learning experiments and improve its own optimization strategies based on what it learns. On a standard benchmark, MLExpert nearly doubled the success rate of its base model—from 38% to 73%—matching the performance of much larger frontier AI systems, and its learned skills transferred to other optimization tasks beyond its training domain.
As AI systems increasingly optimize themselves rather than waiting for human engineers to improve them, this work demonstrates a practical path toward AI that can accelerate its own development cycles. This could compress months of experimentation into days, making advanced AI capabilities faster and cheaper to build—but also raising questions about oversight as these systems operate with less human intervention.