CME-CAD: Reinforcement Learning for CAD Code Generation
Published:Dec 29, 2025 09:37
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of automating CAD model generation, a crucial task in industrial design. It proposes a novel reinforcement learning paradigm, CME-CAD, to overcome limitations of existing methods that often produce non-editable or approximate models. The introduction of a new benchmark, CADExpert, with detailed annotations and expert-generated processes, is a significant contribution, potentially accelerating research in this area. The two-stage training process (MEFT and MERL) suggests a sophisticated approach to leveraging multiple expert models for improved accuracy and editability.
Key Takeaways
- •Proposes CME-CAD, a novel reinforcement learning approach for CAD code generation.
- •Addresses limitations of existing methods in generating editable and precise CAD models.
- •Introduces CADExpert, a new open-source benchmark with detailed annotations.
- •Employs a two-stage training process: Multi-Expert Fine-Tuning (MEFT) and Multi-Expert Reinforcement Learning (MERL).
Reference
“The paper introduces the Heterogeneous Collaborative Multi-Expert Reinforcement Learning (CME-CAD) paradigm, a novel training paradigm for CAD code generation.”