Reinforcing VLMs to Use Tools for Detailed Visual Reasoning Under Resource Constraints
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916880564879360 |
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| author | Kumar, Sunil Zhao, Bowen Dirac, Leo Varshavskaya, Paulina |
| author_facet | Kumar, Sunil Zhao, Bowen Dirac, Leo Varshavskaya, Paulina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14821 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcing VLMs to Use Tools for Detailed Visual Reasoning Under Resource Constraints Kumar, Sunil Zhao, Bowen Dirac, Leo Varshavskaya, Paulina Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Reinforcing VLMs to Use Tools for Detailed Visual Reasoning Under Resource Constraints |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.14821 |