AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning

TL;DR

AdaReasoner is a multimodal model that learns to dynamically select and coordinate tools for visual reasoning, improving performance and generalization without explicit supervision.

Contribution

It introduces a scalable data pipeline, a reinforcement learning algorithm, and an adaptive mechanism for dynamic tool orchestration in multimodal reasoning models.

Findings

Achieves +24.9% improvement on average over baseline models.

Demonstrates strong tool-adaptive and generalization behaviors.

Outperforms proprietary systems like GPT-5 on multiple benchmarks.

Abstract

When humans face problems beyond their immediate capabilities, they rely on tools, providing a promising paradigm for improving visual reasoning in multimodal large language models (MLLMs). Effective reasoning, therefore, hinges on knowing which tools to use, when to invoke them, and how to compose them over multiple steps, even when faced with new tools or new tasks. We introduce \textbf{AdaReasoner}, a family of multimodal models that learn tool use as a general reasoning skill rather than as tool-specific or explicitly supervised behavior. AdaReasoner is enabled by (i) a scalable data curation pipeline exposing models to long-horizon, multi-step tool interactions; (ii) Tool-GRPO, a reinforcement learning algorithm that optimizes tool selection and sequencing based on end-task success; and (iii) an adaptive learning mechanism that dynamically regulates tool usage. Together, these components allow models to infer tool utility from task context and intermediate outcomes, enabling coordination of multiple tools and generalization to unseen tools. Empirically, AdaReasoner exhibits strong tool-adaptive and generalization behaviors: it autonomously adopts beneficial tools, suppresses irrelevant ones, and adjusts tool usage frequency based on task demands, despite never being explicitly trained to do so. These capabilities translate into state-of-the-art performance across challenging benchmarks, improving the 7B base model by +24.9\% on average and surpassing strong proprietary systems such as GPT-5 on multiple tasks, including VSP and Jigsaw.