pith:K6JGKNLI
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning
Reinforcement learning on visual tool feedback lets a small LVLM learn adaptive tool-use policies that outperform supervised training and some larger models on chart reasoning.
arxiv:2505.08617 v2 · 2025-05-13 · cs.CV
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Claims
Our RL-trained agent, built upon a Qwen2-VL-2B, significantly outperforms its SFT-initialized counterpart (+28.83 points) and surpasses established supervised tool-learning baselines like Taco and CogCom by an average of +12.7 points. Notably, it also surpasses prominent closed-source models like GPT-4.1 by +8.68 accuracy points.
The assumption that feedback from tool interactions on chart reasoning tasks will produce policies that generalize to other visual domains and tool sets without additional tuning or domain-specific reward shaping.
OpenThinkIMG and V-ToolRL enable LVLMs to learn adaptive visual tool use via RL, yielding a Qwen2-VL-2B agent that beats its SFT version by 28.83 points and GPT-4.1 by 8.68 points on chart reasoning.
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| First computed | 2026-05-17T23:38:46.421271Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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