Vision-Language Models (VLMs) remain limited in high-precision numerical prediction for 3D scene understanding. In continuous numerical tasks, existing reinforcement learning methods such as GRPO are sample-inefficient: binary rewards fail to distinguish near-miss predictions from incorrect ones, while group-relative normalization can further weaken informative reward differences when sample groups are uniformly poor. Consequently, near-miss samples contribute little to policy updates despite carrying valuable learning signal. We propose Smooth Numerical Reward Activation (SNRA) and Absolute-Preserving GRPO (AP-GRPO) to address this issue. SNRA converts raw numerical errors into dense rewards via a dynamically parameterized sigmoid, while AP-GRPO modulates group-relative advantages with an absolute reward factor to emphasize informative near-miss samples and suppress uniformly uninformative groups. This improves the effective training contribution of near-miss predictions and substantially enhances data efficiency. On our Numerical3D-50k dataset, AP-GRPO achieves performance comparable to large-scale supervised learning across five benchmarks and four VLM architectures, without architectural modifications.