VisTA, a reinforcement learning framework, enhances visual reasoning by autonomously selecting and combining tools from a diverse library without extensive human supervision.
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.
