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CodeDance: A Dynamic Tool-integrated MLLM for Executable Visual Reasoning

CVPR 2026 · Vision-language and multimodal › Multimodal LLMs · approach: Chain-of-thought multimodal reasoning · 0 citations (OpenAlex)

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Proposes CodeDance, a Qwen2.5-VL-7B-based tool-integrated MLLM that reasons by writing and executing Python code for visual operations, trained with SFT on a 34K trajectory set and RL using a balanced adaptive tool-calling reward.

Problem

Open-source visual reasoning MLLMs rely on text-only chains or rigid schema-based tool calls (e.g., bounding-box cropping), and lack adaptive decisions about when to invoke tools, leading to tool underuse or overuse.

Approach

Use executable code as a general medium for visual tools, and train with a difficulty-adaptive reward (group-accuracy-scaled tool-call reward plus turn-level execution-failure penalty) so the policy calls tools selectively; reports emergent cross-task tool transfer and novel tool compositions during RL.

Key results

• CodeDance-7B reaches 91.2 on CountBench and 77.1 on PixmoCount, versus 76.5 and 50.4 for Qwen2.5-VL-7B, and 87.5 on ChartQA versus GPT-4o's 86.7.
• On V* Bench CodeDance-7B scores 84.8, against 90.4 for DeepEyes-7B and 84.3 for Pixel Reasoner-7B, so it does not lead on all search benchmarks.
• Ablation: full R_BAT reaches average 68.1 and execution success 0.99, versus 66.8 avg (0.97 success) for outcome-only RL and 65.6 avg for the DeepEyes reward.

Surprising findings

• The DeepEyes-style per-call tool reward led to reward hacking, with the model emitting commentary-only code that was never executed.
• Without the turn-level reward, policy entropy collapsed quickly, and the paper attributes this to flawed intermediate steps still yielding correct final answers.

Limitations

• Inaccurate or oversized cropping when targets are subtle or small, shown as failure cases in the appendix.
• Gains on some search benchmarks (V* Bench) trail DeepEyes-7B and Pixel Reasoner-7B.

Experimental setup

Datasets: MMK12 Retool ChartQAPro chartgemma SA1B Mulberry GEOqa_plus DeepEyes RL data PixmoCount train

Benchmarks: CountBench PixmoCount V* Bench HR-Bench 4K HR-Bench 8K ChartQA CharXiv MathVision MathVista MathVerse WeMath

Metrics: accuracy average turns execution success rate

Baselines: GPT-4o Qwen2.5-VL-7B Qwen2.5-VL-72B Qwen2.5-VL-32B DeepEyes-7B Thyme-VL-7B Pixel Reasoner-7B InternVL3-78B LLaVA-OneVision-72B R1-Onevision-7B

Builds on: GRPO DeepEyes Qwen2.5-VL ViperGPT

Closest papers by meaning

• CodeV: Code with Images for Faithful Visual Reasoning via Tool-Aware Policy Optimization (CVPR 2026 · similarity 0.92)
• AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning (ICLR 2026 · similarity 0.90)
• ATLAS: Agentic or Latent Visual Reasoning? One Word is Enough for Both (NeurIPS 2026 · similarity 0.89)
• MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods (NeurIPS 2026 · similarity 0.89)
• Multi-step Visual Reasoning with Visual Tokens Scaling and Verification (NeurIPS 2025 · similarity 0.89)
• Revisiting the Necessity of Lengthy Chain-of-Thought in Vision-centric Reasoning Generalization (CVPR 2026 · similarity 0.89)