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arXiv · 2506.14821

Reinforcing VLMs to Use Tools for Detailed Visual Reasoning Under Resource Constraints

Sunil Kumar·Bowen Zhao·Leo Dirac·Paulina Varshavskaya
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Abstract

Despite tremendous recent advances in large model reasoning ability, vision-language models (VLMs) still struggle with detailed visual reasoning, especially when compute resources are limited. To address this challenge, we draw inspiration from methods like Deepseek-r1 for VLMs and train smaller-scale models with Group Relative Policy Optimization (GRPO) to use external tools such as zoom. The greatest benefit is obtained with a combination of GRPO learning, a simple reward structure, a simplified tool-calling interface, allocating additional tokens to the result of the tool call, and a training data mix that over-represents visually difficult examples. Compared to similarly-sized baseline models, our method achieves better performance on some visual question-answering (VQA) tasks, thanks to the detailed visual information gathered from the external tool.

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Sunil Kumar, Bowen Zhao, Leo Dirac, Paulina Varshavskaya. 2025-06-10. Reinforcing VLMs to Use Tools for Detailed Visual Reasoning Under Resource Constraints. https://arxiv.org/abs/2506.14821

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