VisualToolAgent (VisTA): A Reinforcement Learning Framework for Visual Tool Selection

• URL: http://arxiv.org/abs/2505.20289v2
• Date: Sat, 19 Jul 2025 05:24:59 GMT
• Title: VisualToolAgent (VisTA): A Reinforcement Learning Framework for Visual Tool Selection
• Authors: Zeyi Huang, Yuyang Ji, Anirudh Sundara Rajan, Zefan Cai, Wen Xiao, Haohan Wang, Junjie Hu, Yong Jae Lee,
• Abstract summary: VisTA is a new reinforcement learning framework that empowers visual agents to dynamically explore, select, and combine tools from a diverse library based on empirical performance.<n>We show that VisTA achieves substantial performance gains over training-free baselines.<n>These results highlight VisTA's ability to enhance generalization, adaptively utilize diverse tools, and pave the way for flexible, experience-driven visual reasoning systems.
• Score: 47.259066449806866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce VisTA, a new reinforcement learning framework that empowers visual agents to dynamically explore, select, and combine tools from a diverse library based on empirical performance. Existing methods for tool-augmented reasoning either rely on training-free prompting or large-scale fine-tuning; both lack active tool exploration and typically assume limited tool diversity, and fine-tuning methods additionally demand extensive human supervision. In contrast, VisTA leverages end-to-end reinforcement learning to iteratively refine sophisticated, query-specific tool selection strategies, using task outcomes as feedback signals. Through Group Relative Policy Optimization (GRPO), our framework enables an agent to autonomously discover effective tool-selection pathways without requiring explicit reasoning supervision. Experiments on the ChartQA, Geometry3K, and BlindTest benchmarks demonstrate that VisTA achieves substantial performance gains over training-free baselines, especially on out-of-distribution examples. These results highlight VisTA's ability to enhance generalization, adaptively utilize diverse tools, and pave the way for flexible, experience-driven visual reasoning systems.

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