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    <abstract>Recent studies posit that Reinforcement Learning with Verifiable Rewards (RLVR) primarily amplifies behaviors inherent to the pre-training distribution rather than inducing new capabilities, but these insights are predominantly limited to language-only domains, leaving the dynamics of visual-centric spatial reasoning under-explored. To examine the impact of RLVR on the capability boundaries of Vision-Language Models (VLMs), we introduce Ariadne, a controlled framework based on synthetic maze navigation where the reasoning difficulty is precisely regulated by path length and the number of turns. We demonstrate that applying RLVR extends the spatial reasoning boundary, achieving success on problems where the base policy VLM consistently attains 0% accuracy despite increasing pass@k sampling budgets, indicating that the optimized policy successfully navigates search spaces that were effectively unreachable by the base distribution. Furthermore, despite being trained exclusively on synthetic mazes, we evaluate the model on two real-world navigation benchmarks (MapBench and ReasonMap) in a zero-shot setting. The observed improvements in these out-of-domain tasks suggest genuine spatial reasoning capability expansion rather than mere sampling efficiency. Our code is available at: https://github.com/MingheShen/Ariadne</abstract>
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%0 Conference Proceedings
%T Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models
%A Shen, Minghe
%A Zhi, Zhuo
%A Liu, Chonghan
%A Xing, Shuo
%A Tu, Zhengzhong
%A Liu, Che
%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 shen-etal-2026-rlvr
%X Recent studies posit that Reinforcement Learning with Verifiable Rewards (RLVR) primarily amplifies behaviors inherent to the pre-training distribution rather than inducing new capabilities, but these insights are predominantly limited to language-only domains, leaving the dynamics of visual-centric spatial reasoning under-explored. To examine the impact of RLVR on the capability boundaries of Vision-Language Models (VLMs), we introduce Ariadne, a controlled framework based on synthetic maze navigation where the reasoning difficulty is precisely regulated by path length and the number of turns. We demonstrate that applying RLVR extends the spatial reasoning boundary, achieving success on problems where the base policy VLM consistently attains 0% accuracy despite increasing pass@k sampling budgets, indicating that the optimized policy successfully navigates search spaces that were effectively unreachable by the base distribution. Furthermore, despite being trained exclusively on synthetic mazes, we evaluate the model on two real-world navigation benchmarks (MapBench and ReasonMap) in a zero-shot setting. The observed improvements in these out-of-domain tasks suggest genuine spatial reasoning capability expansion rather than mere sampling efficiency. Our code is available at: https://github.com/MingheShen/Ariadne
%U https://aclanthology.org/2026.acl-long.2102/
%P 45317-45331

Markdown (Informal)

[Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models](https://aclanthology.org/2026.acl-long.2102/) (Shen et al., ACL 2026)

• Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models (Shen et al., ACL 2026)

ACL

• Minghe Shen, Zhuo Zhi, Chonghan Liu, Shuo Xing, Zhengzhong Tu, and Che Liu. 2026. Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45317–45331, San Diego, California, United States. Association for Computational Linguistics.