AI RESEARCH
Reinforcing 3D Understanding in Point-VLMs via Geometric Reward Credit Assignment
arXiv CS.CV
•
ArXi:2604.21160v1 Announce Type: new Point-Vision-Language Models promise to empower embodied agents with executable spatial reasoning, yet they frequently succumb to geometric hallucination where predicted 3D structures contradict the observed 2D reality. We identify a key cause of this failure not as a representation bottleneck but as a structural misalignment in reinforcement learning, where sparse geometric tokens are drowned out by noisy and broadcasted sequence-level rewards.