Furkejuvvon:
| Váldodahkkit: | , , , |
|---|---|
| Materiálatiipa: | Preprint |
| Almmustuhtton: |
2025
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| Fáttát: | |
| Liŋkkat: | https://arxiv.org/abs/2506.14821 |
| Fáddágilkorat: |
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Sisdoallologahallan:
- 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.