# Hierarchical Balanced Advantage

Text-driven CAD generation has long been optimized and evaluated on geometric similarity or visual consistency, yet a correct-looking shape does not guarantee a usable structure. PhyCAD runs executable CadQuery programs through a deterministic CAD–CAE pipeline that judges program executability, geometric and dimensional compliance, and finite-element stress, displacement and mass constraints in stages, then reshapes the GRPO training signal with a hierarchical advantage. On a held-out evaluation set, several open models trained with supervised fine-tuning followed by this reinforcement learning raise joint geometric-and-physical success from 21.76%–37.36% to 39.49%–58.05%.

Text-driven CAD generation has long been optimized and evaluated on geometric similarity or visual consistency, yet a correct-looking shape does not guarantee a usable structure. PhyCAD runs executable CadQuery programs through a deterministic CAD–CAE pipeline that judges program executability, geometric and dimensional compliance, and finite-element stress, displacement and mass constraints in stages, then reshapes the GRPO training signal with a hierarchical advantage. On a held-out evaluation set, several open models trained with supervised fine-tuning followed by this reinforcement learning raise joint geometric-and-physical success from 21.76%–37.36% to 39.49%–58.05%.