Generative Vision-Language Models (VLMs) express bounding boxes as coordinate sequences, whose token identities often leave axis and place value implicit. We study how explicitly encoding these roles improves visual grounding. Hi-Token assigns a distinct token to each combination of axis, decimal place, and digit value, representing a box with 12 tokens drawn from 60 types. This design exposes coarse-to-fine coordinate structure and increases token reuse relative to atomic location tokens while retaining the existing VLM architecture. Hi-GAR complements this representation with a geometry-based reward for Group Relative Policy Optimization (GRPO), combining box overlap, coordinate accuracy at multiple scales, strict-IoU bonuses, and validity gating. Matched training comparisons show that Hi-Token improves localization throughout the evaluated IoU range, with gains across three VLM backbones. Hi-Token also outperforms an additionally tuned atomic-token baseline on the RefCOCO family before reinforcement learning. Hi-GAR further improves localization and is used only during training. The resulting Hi-R1 model achieves higher values than strong specialist baselines on most reported metrics. Token-frequency and geometric diagnostics characterize the representation and reward stages. These results establish explicit axis and place-value encoding as an effective design for generative visual grounding. Project page: https://xyzzzh.github.io/Hi-Token/

翻译：暂无翻译