Tag
EvoCUA-1.5 introduces an online reinforcement learning framework for multi-turn computer-use agents, achieving a 63.2% success rate on OSWorld-Verified and outperforming comparable open-weight models up to 35B parameters through step-level policy optimization and dynamic curriculum learning.
This paper proposes PTD-PO, a privileged tutoring distillation framework that provides dense token-level supervision for reinforcement learning with verifiable rewards in multimodal reasoning tasks, without exposing the answer. It uses structured hints and a Top-K JS divergence objective to stabilize training, consistently outperforming existing methods on 2B-8B LVLMs.