GeoDRL: A Self-Learning Framework for Geometry Problem Solving using Reinforcement Learning in Deductive Reasoning (2023.findings-acl)
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| Challenge: | Existing methods for automated geometry problem solving lack labeled data. |
| Approach: | They propose a framework that integrates logic graph deduction and deep reinforcement learning to optimize geometry reasoning as a Markov Decision Process. |
| Outcome: | The proposed framework improves accuracy and interpretability in the Geometry3K dataset while maintaining correctness. |
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| Challenge: | Existing parameter-efficient methods for RLVR face limitations . low-rank adaptation methods do not account for the distinct optimization dynamics . |
| Approach: | They propose a low-rank adaptation method tailored for RLVR that exploits the anisotropic structure of RL update subspace and extracts its principal directions via Singular Value Decomposition (SVD). |
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GR1: Reinforcement-Enhanced LLM for Geoscience Reasoning (2026.findings-acl)
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Yule Xie, Jiaxin Ding, Cheng Deng, Shiqing Gao, Junran Zhang, Sibo Zhang, Zeyuan Wang, Ke Wu, Xin Ding, Luoyi Fu, Meng Jin, Xinbing Wang
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Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning (2021.acl-long)
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| Challenge: | Existing methods for solving geometric problems are either small in scale or not publicly available. |
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| Challenge: | Recent advances in reinforcement learning (RL) have enhanced the reasoning abilities of large language models, but the impact on multimodal LLMs is limited. |
| Approach: | They propose a two-stage RL framework that enhances visual perception and fosters reasoning capabilities. |
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UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression (2022.emnlp-main)
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| Challenge: | Existing work on geometry problem solving treats calculation and proving as two specific tasks hindering a deep model to unify reasoning ability on multiple math tasks. |
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GeometryZero: Advancing Geometry Solving via Group Contrastive Policy Optimization (2026.findings-acl)
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| Challenge: | Existing methods for auxiliary construction training are expensive and underperform . Existing Corresponding Author training methods lack self-correction capabilities in reasoning chains. |
| Approach: | They propose a reinforcement learning framework that rewards auxiliary construction with geometric reasoning by grouping construction rewards with a Length Reward. |
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Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning (2026.eacl-long)
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Syeda Nahida Akter, Shrimai Prabhumoye, Matvei Novikov, Seungju Han, Ying Lin, Evelina Bakhturina, Eric Nyberg, Yejin Choi, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
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A Survey of Deep Learning for Geometry Problem Solving (2026.acl-long)
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| Challenge: | Recent surge in deep learning technologies has significantly accelerated research in this area. |
| Approach: | They propose a comprehensive summary of the relevant tasks in geometry problem solving and a review of related deep learning methods. |
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GeoLAN: Geometric Learning of Latent Explanatory Directions in Large Language Models (2026.findings-acl)
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| Challenge: | Large language models lack transparency and are often unable to explain causal relationships . |
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GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models (2025.findings-naacl)
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| Challenge: | Various vision-language models (VLMs) have made significant progress in multimodal tasks, but they still struggle with geometry problems. |
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