Closing the Spatial Execution Gap in Digital Whiteboards via Verifiable Reinforcement Learning
Chang Liu Colorado School of Mines, Benjamin Wagley Colorado School of Mines, Zibo Wang, Mehmet E. Belviranli Colorado School of Mines, Bo Wu Colorado School of Mines
While multi-modal large language models such as GPT-5 demonstrate exceptional general understanding, they suffer from a fundamental Spatial Execution Gap, failing to translate visual semantics into precise, schema-valid coordinate operations in interactive environments. In this work, we show that model scale alone cannot close this gap; instead, verifiable structured reasoning provides the key to spatial precision. We present a comprehensive pipeline that leverages Group Relative Policy Optimization to enforce a strict Identify-Reason-Verify protocol, effectively shifting the computational burden from parameters to test-time reasoning. By utilizing a multi-agent system to distill optimal reasoning schemas and training on execution-verifiable rewards, our specialized 3B agent achieves 100% format coherence and 81.12% operation accuracy on digital whiteboard tasks. Crucially, our approach outperforms a state-of-the-art frontier model, GPT-5, by 16.75% in operation accuracy. The results suggest that for complex user interface manipulation, small, RL-aligned models with dedicated reasoning protocols are superior to generalist frontier models, offering a promising direction for building reliable web agents.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表