Xiang Zhu

Token Importance-Aware Policy Optimization is proposed, a novel reinforcement learning strategy that explicitly leverages token-importance awareness and identifies core tokens based on token dependency and reweights a trajectory's advantage according to its overall dependencies.

EVEREST's egocentric exploration strategy revolutionizes urban segmentation by actively refining boundary detection, outperforming traditional passive methods.