Jin-Xin Shi

Autonomous ML engineers can now achieve state-of-the-art performance on complex tasks by learning from long, outcome-guided experimental trajectories, outperforming larger models on benchmarks.

Achieving trillion-parameter performance with just 35 billion parameters by scaling agent horizons reveals a new frontier in model efficiency.