Yu-Pei Wang

NAS-PO: Native Attention-Strategy Policy Optimization for Vision-Language Models

Reinforcement learning with verifiable rewards (RLVR) has become an effective post-training paradigm for vision-language models (VLMs). Standard RLVR, however, assigns identical outcome-derived credit to all generated tokens regardless of their perceptual relevance. Recent perception-aware methods estimate visual relia...