Training AI agents on long experimental workflows requires learning from intermediate outcomes, not just final results—this paper shows how to extract useful learning signals from expensive, multi-step ML engineering tasks.
MLExpert is a 35B AI model trained to autonomously manage machine learning experiments and optimization. Using a novel training approach combining supervised learning from expert trajectories and reinforcement learning from experiment outcomes, it learns to make better decisions across long sequences of costly ML tasks, improving performance from 38% to 73% on benchmarks.