PLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesis

Yuanbo Li; Dule Shu; Yanying Chen; Matt Klenk; Daniel Ritchie

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arXiv · 2602.12561

PLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesis

Yuanbo Li·Dule Shu·Yanying Chen·Matt Klenk·Daniel Ritchie

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Abstract

Recovering Computer-Aided Design (CAD) programs from 3D geometries is a widely studied problem. Recent advances in large language models (LLMs) have enabled progress in CAD program synthesis, but existing methods rely on supervised training with paired shape-program data, which is often unavailable. We introduce PLLM, a self-training framework for CAD program synthesis from unlabeled 3D shapes. Given a pre-trained CAD-capable LLM and a shape dataset, PLLM iteratively samples candidate programs, selects high-fidelity executions, and augments programs to construct synthetic program-shape pairs for fine-tuning. We experiment on adapting CAD-Recode from DeepCAD to the unlabeled ABC dataset show consistent improvements in geometric fidelity and program diversity.

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BibTeX RIS

Yuanbo Li, Dule Shu, Yanying Chen, Matt Klenk, Daniel Ritchie. 2026-02-13. PLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesis. https://arxiv.org/abs/2602.12561

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