V-Thinker: Interactive Thinking with Images
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.
690d5b2aad2597bf6c464c9a
Runqi Qiao
690d5b2aad2597bf6c464c9b
Qiuna Tan
690d5b2aad2597bf6c464c9c
Minghan Yang
690d5b2aad2597bf6c464c9d
Guanting Dong
690d5b2aad2597bf6c464c9e
Peiqing Yang
690d5b2aad2597bf6c464c9f
Shiqiang Lang
690d5b2aad2597bf6c464ca0
Enhui Wan
690d5b2aad2597bf6c464ca1
Xiaowan Wang
690d5b2aad2597bf6c464ca2
Yida Xu
690d5b2aad2597bf6c464ca3
Lan Yang
690d5b2aad2597bf6c464ca4
Chong Sun
690d5b2aad2597bf6c464ca5
Chen Li
690d5b2aad2597bf6c464ca6
Honggang Zhang
V-Thinker: Interactive Thinking with Images
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.
V-Thinker, a multimodal reasoning assistant using reinforcement learning, enhances image-interactive thinking by synthesizing datasets and aligning perception for improved performance in vision-centric tasks.
multimodal models image interaction long-horizon reasoning Thinking with Images image-interactive thinking end-to-end reinforcement learning Data Evolution Flywheel Visual Progressive Training Curriculum point-level supervision two-stage reinforcement learning VTBench vision-centric interactive reasoning