title: cadrille: Multi-modal CAD Reconstruction with Online Reinforcement
    Learning
summary: Computer-Aided Design (CAD) plays a central role in engineering and
  manufacturing, making it possible to create precise and editable 3D models.
  Using a variety of sensor or user-provided data as inputs for CAD
  reconstruction can democratize access to design applications. However, existing
  methods typically focus on a single input modality, such as point clouds,
  images, or text, which limits their generalizability and robustness. Leveraging
  recent advances in vision-language models (VLM), we propose a multi-modal CAD
  reconstruction model that simultaneously processes all three input modalities.
  Inspired by large language model (LLM) training paradigms, we adopt a two-stage
  pipeline: supervised fine-tuning (SFT) on large-scale procedurally generated
  data, followed by reinforcement learning (RL) fine-tuning using online
  feedback, obtained programatically. Furthermore, we are the first to explore RL
  fine-tuning of LLMs for CAD tasks demonstrating that online RL algorithms such
  as Group Relative Preference Optimization (GRPO) outperform offline
  alternatives. In the DeepCAD benchmark, our SFT model outperforms existing
  single-modal approaches in all three input modalities simultaneously. More
  importantly, after RL fine-tuning, cadrille sets new state-of-the-art on three
  challenging datasets, including a real-world one.
paper_authors._id: 6839533ae7922379b361b3cb
paper_authors.name: Maksim Kolodiazhnyi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:46:40.766Z
paper_authors._id: 665b10fb270e47e678f2ddf1
paper_authors.avatarUrl: /avatars/1bc7a9211acf767f7bfca998c24315a0.svg
paper_authors.fullname: max
paper_authors.type: user
paper_authors.user: maksimko123
paper_authors._id: 6839533ae7922379b361b3cc
paper_authors.name: Denis Tarasov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:46:43.447Z
paper_authors._id: 63f613cab29015adc3393d60
paper_authors.avatarUrl: /avatars/d8402762bf8f96270a729b048f4bc557.svg
paper_authors.fullname: Denis Tarasov
paper_authors.type: user
paper_authors.user: Adagrad
paper_authors._id: 6839533ae7922379b361b3cd
paper_authors.name: Dmitrii Zhemchuzhnikov
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-30T06:42:03.218Z
paper_authors._id: 67d5a331eab66ce9cb01bae4
paper_authors.avatarUrl: /avatars/3ed437c874889e8a6db66c3ef88a60c0.svg
paper_authors.fullname: DMITRII ZHEMCHUZHNIKOV
paper_authors.type: user
paper_authors.user: zhemchuzhnikov
paper_authors._id: 6839533ae7922379b361b3ce
paper_authors.name: Alexander Nikulin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:46:46.258Z
paper_authors._id: 636824b2856a92b278936457
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/636824b2856a92b278936457/fzQgPTUjPKg1XZ73L3gzw.jpeg
paper_authors.fullname: Alexander Nikulin
paper_authors.type: user
paper_authors.user: Howuhh
paper_authors._id: 6839533ae7922379b361b3cf
paper_authors.name: Ilya Zisman
paper_authors._id: 6839533ae7922379b361b3d0
paper_authors.name: Anna Vorontsova
paper_authors._id: 6839533ae7922379b361b3d1
paper_authors.name: Anton Konushin
paper_authors._id: 6839533ae7922379b361b3d2
paper_authors.name: Vladislav Kurenkov
paper_authors._id: 6839533ae7922379b361b3d3
paper_authors.name: Danila Rukhovich
paper_title: cadrille: Multi-modal CAD Reconstruction with Online Reinforcement
    Learning
paper_summary: Computer-Aided Design (CAD) plays a central role in engineering and
  manufacturing, making it possible to create precise and editable 3D models.
  Using a variety of sensor or user-provided data as inputs for CAD
  reconstruction can democratize access to design applications. However, existing
  methods typically focus on a single input modality, such as point clouds,
  images, or text, which limits their generalizability and robustness. Leveraging
  recent advances in vision-language models (VLM), we propose a multi-modal CAD
  reconstruction model that simultaneously processes all three input modalities.
  Inspired by large language model (LLM) training paradigms, we adopt a two-stage
  pipeline: supervised fine-tuning (SFT) on large-scale procedurally generated
  data, followed by reinforcement learning (RL) fine-tuning using online
  feedback, obtained programatically. Furthermore, we are the first to explore RL
  fine-tuning of LLMs for CAD tasks demonstrating that online RL algorithms such
  as Group Relative Preference Optimization (GRPO) outperform offline
  alternatives. In the DeepCAD benchmark, our SFT model outperforms existing
  single-modal approaches in all three input modalities simultaneously. More
  importantly, after RL fine-tuning, cadrille sets new state-of-the-art on three
  challenging datasets, including a real-world one.
paper_ai_summary: A multi-modal CAD reconstruction model leveraging vision-language models and reinforcement learning achieves state-of-the-art performance across various datasets, including real-world ones.
paper_ai_keywords: vision-language models multi-modal large language model supervised fine-tuning reinforcement learning Group Relative Preference Optimization DeepCAD benchmark