Fengying Xie

Abstract skills can transform reinforcement learning by providing dense supervision when traditional reward signals are inadequate, leading to significant performance gains.

Open-source MLLMs can now achieve state-of-the-art accuracy on complex tabular reasoning tasks, even outperforming models 18x their size, by explicitly penalizing visual hallucinations and shortcut guessing through process-supervised RL.