@inproceedings{liu-etal-2026-closing,
title = "Closing the Spatial Execution Gap in Digital Whiteboards via Verifiable Reinforcement Learning",
author = "Liu, Chang and
Wagley, Benjamin and
Wang, Zibo and
Belviranli, Mehmet E. and
Wu, Bo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.630/",
pages = "13834--13849",
ISBN = "979-8-89176-390-6",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Closing the Spatial Execution Gap in Digital Whiteboards via Verifiable Reinforcement Learning
%A Liu, Chang
%A Wagley, Benjamin
%A Wang, Zibo
%A Belviranli, Mehmet E.
%A Wu, Bo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F liu-etal-2026-closing
%X 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.
%U https://aclanthology.org/2026.acl-long.630/
%P 13834-13849
Markdown (Informal)
[Closing the Spatial Execution Gap in Digital Whiteboards via Verifiable Reinforcement Learning](https://aclanthology.org/2026.acl-long.630/) (Liu et al., ACL 2026)
ACL