VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use (2505.19255v3)

Abstract: Reinforcement Learning Finetuning (RFT) has significantly advanced the reasoning capabilities of LLMs by enabling long chains of thought, self-correction, and effective tool use. While recent works attempt to extend RFT to vision-LLMs (VLMs), these efforts largely produce text-only reasoning conditioned on static image inputs, falling short of true multimodal reasoning in the response. In contrast, test-time methods like Visual Sketchpad incorporate visual steps but lack training mechanisms. We introduce VTool-R1, the first framework that trains VLMs to generate multimodal chains of thought by interleaving text and intermediate visual reasoning steps. VTool-R1 integrates Python-based visual editing tools into the RFT process, enabling VLMs to learn when and how to generate visual reasoning steps that benefit final reasoning. Trained with outcome-based rewards tied to task accuracy, our approach elicits strategic visual tool use for reasoning without relying on process-based supervision. Experiments on structured visual question answering over charts and tables show that VTool-R1 enhances reasoning performance by teaching VLMs to "think with images" and generate multimodal chain of thoughts with tools.