title: VisualToolAgent (VisTA): A Reinforcement Learning Framework for Visual
    Tool Selection
summary: 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.
paper_authors._id: 68367517de8f6638c15ddccd
paper_authors.name: Zeyi Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:57:42.099Z
paper_authors._id: 683674598a36b9fa7f28db08
paper_authors.avatarUrl: /avatars/81296476ddfa2d3ff6f523186d051afb.svg
paper_authors.fullname: ZeyiHuang
paper_authors.type: user
paper_authors.user: ZeyiHuang1010
paper_authors._id: 68367517de8f6638c15ddcce
paper_authors.name: Yuyang Ji
paper_authors._id: 68367517de8f6638c15ddccf
paper_authors.name: Anirudh Sundara Rajan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:57:34.543Z
paper_authors._id: 6496b347b8d4efc75b02e2fa
paper_authors.avatarUrl: /avatars/2aa6b168e5d1aeb7b9e3481c826450a5.svg
paper_authors.fullname: Anirudh Sundara Rajan
paper_authors.type: user
paper_authors.user: AniSundar18
paper_authors._id: 68367517de8f6638c15ddcd0
paper_authors.name: Zefan Cai
paper_authors._id: 68367517de8f6638c15ddcd1
paper_authors.name: Wen Xiao
paper_authors._id: 68367517de8f6638c15ddcd2
paper_authors.name: Junjie Hu
paper_authors._id: 68367517de8f6638c15ddcd3
paper_authors.name: Yong Jae Lee
paper_title: VisualToolAgent (VisTA): A Reinforcement Learning Framework for Visual
    Tool Selection
paper_summary: 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.
paper_ai_summary: VisTA, a reinforcement learning framework, enhances visual reasoning by autonomously selecting and combining tools from a diverse library without extensive human supervision.
paper_ai_keywords: reinforcement learning end-to-end reinforcement learning tool-augmented reasoning Group Relative Policy Optimization (GRPO) ChartQA Geometry3K BlindTest generalization adaptive tool utilization