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.
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Runqi Qiao
claimed_verified
2025-11-10T09:32:03.684Z
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QRQ
RichardQRQ
user
RichardQRQ
690d5b2aad2597bf6c464c9b
Qiuna Tan
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Minghan Yang
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Guanting Dong
claimed_verified
2026-02-27T16:47:09.774Z
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KABI
dongguanting
user
dongguanting
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Peiqing Yang
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Shiqiang Lang
claimed_verified
2025-12-01T16:25:03.487Z
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Bruceq
langshiqiang
user
langshiqiang
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Enhui Wan
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Xiaowan Wang
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Yida Xu
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Lan Yang
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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