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ReCAD: Reinforcement Learning Enhanced Parametric CAD Model Generation with Vision-Language Models

AAAI 2026 · Vision-language and multimodal › Multimodal LLMs · approach: Reinforcement learning for visual reasoning · 3 citations (OpenAlex)

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ReCAD fine-tunes Qwen2.5-VL-7B on parameterized CAD code and then applies GRPO with code-guided off-policy rollouts and hierarchical primitive curriculum to generate parametric CAD models from text or images.

Problem

Generating precise, editable parametric CAD models from text or image inputs, where prior methods rely on SFT knowledge injection, generalize poorly out-of-distribution, and produce imprecise parameters.

Approach

Rewrite hardcoded CAD scripts into parameterized code with a VLM (filtered by DINOv2 similarity), use that code to generate descriptions for SFT, then run RL (GRPO) where parameterized code guides rollouts for hard questions, with a curriculum over primitive hierarchy and a reward combining IoU and image-feature similarity.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: DeepCAD Fusion 360 Gallery UltraChat OpenCodeReasoning Text2CAD descriptions CAD-LLaMA descriptions

Benchmarks: DeepCAD Fusion 360 Gallery

Metrics: P-F1 Chamfer Distance (median, mean) Invalidity Ratio IOU_best

Baselines: GPT-4o DeepSeek-V3 Qwen3-235B-A22B CAD-Translator Text2CAD CAD-LLaMA CAD-Coder Gemini-2.5-Pro Qwen-2.5-72B

Builds on: GRPO CAD-Coder CAD-LLaMA DeepCAD Text2CAD

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