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PointRL: Learning Point-Level Vision-Language Grounding from Verifiable Annotation Evidence

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

Vision-language models (VLMs) increasingly rely on point coordinates as a compact and executable interface for visual grounding in GUI interaction, robotic manipulation, and interactive visual systems. However, learning reliable pointing behavior remains difficult because the supervision space is inherently non-unique: many coordinates may be valid within the same target region, while multi-instance instructions require target coverage, count consistency, and duplicate suppression. This work presents PointRL, a verifiable reinforcement learning framework that learns point-level grounding from

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