This repository implements Seg-Zero and VisionReasoner, research projects that train vision-language models for unified visual perception and reasoning. The models generate step-by-step reasoning chains before producing final segmentation outputs. Training relies exclusively on reinforcement learning, enabling emergent test-time reasoning without explicit supervised reasoning data. It supports Qwen2-VL and Qwen2.5-VL model series.