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

Mingyuan Wu, Jingcheng Yang, Jize Jiang, Meitang Li, Kaizhuo Yan, Hanchao Yu, Minjia Zhang, ChengXiang Zhai, Klara Nahrstedt

Keywords: Reinforcement Learning, Vison Lanaguage Model, Reasoning

TL;DR: Reinforcement learning finetuning can enable vision language models to think with intermediate image reasoning steps.

Abstract:

Reinforcement learning finetuning (RFT) has significantly advanced the reasoning capabilities of large language models (LLMs) by enabling long chains of thought, multi-turn self-correction, and effective tool use. While recent works attempt to extend RFT to vision-language models (VLMs), these efforts largely focus on text-only reasoning conditioned on original image inputs, and do not incorporate visual 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 RFT 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 enhance the final output quality. Trained with outcome-based rewards, our approach elicits strategic visual tool use for multi-modal reasoning without relying on process-based supervision. Extensive experiments on structured visual reasoning 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. To support future research in multi-turn multi-modal reasoning, we open-source our code at https://github.com/VTOOL-R1/vtool-r1.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 12245