V-Thinker: Interactive Thinking with Images
Authors: name: Runqi Qiao; name: Qiuna Tan; name: Minghan Yang; name: Guanting Dong; name: Peiqing Yang; name: Shiqiang Lang; name: Enhui Wan; name: Xiaowan Wang; name: Yida Xu; name: Lan Yang; name: Chong Sun; name: Chen Li; name: Honggang Zhang
Published: 2025-11-06 15:32:29

Abstract: Empowering Large Multimodal Models (LMMs) to deeply integrate image
interaction with long-horizon reasoning capabilities remains a long-standing
challenge in this field. Recent advances in vision-centric reasoning explore a
promising "Thinking with Images" paradigm for LMMs, marking a shift from
image-assisted reasoning to image-interactive thinking. While this milestone
enables models to focus on fine-grained image regions, progress remains
constrained by limited visual tool spaces and task-specific workflow designs.
To bridge this gap, we present V-Thinker, a general-purpose multimodal
reasoning assistant that enables interactive, vision-centric thinking through
end-to-end reinforcement learning. V-Thinker comprises two key components: (1)
a Data Evolution Flywheel that automatically synthesizes, evolves, and verifies
interactive reasoning datasets across three dimensions-diversity, quality, and
difficulty; and (2) a Visual Progressive Training Curriculum that first aligns
perception via point-level supervision, then integrates interactive reasoning
through a two-stage reinforcement learning framework. Furthermore, we introduce
VTBench, an expert-verified benchmark targeting vision-centric interactive
reasoning tasks. Extensive experiments demonstrate that V-Thinker consistently
outperforms strong LMM-based baselines in both general and interactive
reasoning scenarios, providing valuable insights for advancing
image-interactive reasoning applications.