让小模型用外部工具做精细视觉推理，资源有限时表现更优
Reinforcing VLMs to Use Tools for Detailed Visual Reasoning Under Resource Constraints
- 用GRPO训练小模型调用工具（如缩放）获取细节信息
- 在多个VQA任务上超越同规模基线模型，提升明显
- 适合资源受限场景下需要精准视觉分析的应用
尽管大型模型的推理能力显著提升，视觉语言模型（VLMs）在计算资源受限时仍难以进行细致的视觉推理。为此，我们借鉴Deepseek-r1的方法，采用分组相对策略优化（GRPO）训练小型模型，使其能够使用外部工具（如缩放）。最佳效果来自结合GRPO学习、简单奖励结构、简化工具调用接口、将额外标记分配给工具调用结果，以及过采样视觉困难样本的训练数据组合。相比同规模基线模型，该方法在部分视觉问答（VQA）任务上表现更佳，得益于从外部工具获取的详细视觉信息。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。