---
title: "Who cites Reinforcement Learning of Theorem Proving"
description: "35 papers of the Elamur Research library cite «Reinforcement Learning of Theorem Proving», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/kaliszyk-2018-reinforcement-learning-theorem-proving/cited-by/"
---
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# Who cites «Reinforcement Learning of Theorem Proving»

35 papers of our library cite it (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · [← back to «Reinforcement Learning of Theorem Proving»](https://md.research.elamur.ai/free/en/papers/kaliszyk-2018-reinforcement-learning-theorem-proving/)

1. **[Solving Quantitative Reasoning Problems with Language Models](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Aitor Lewkowycz, Anders Andreassen, David Dohan, et al.*2022 · 992 citations*
2. [**DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data arXiv:2405.14333**Huajian Xin, Daya Guo, Zhihong Shao, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/xin-2024-deepseek-prover-advancing-theorem-proving/)
3. [**GamePad: A Learning Environment for Theorem Proving arXiv:1806.00608**Daniel Huang, Prafulla Dhariwal, Dawn Song, et al.*2018 · 94 citations*](https://md.research.elamur.ai/free/en/papers/huang-2018-gamepad-learning-environment-theorem-proving/)
4. [**Learning to Prove Theorems via Interacting with Proof Assistants arXiv:1905.09381**Kaiyu Yang, Jia Deng*2019 · 90 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-learning-prove-theorems-interacting-proof/)
5. [**Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving arXiv:2502.07640**Yong Lin, Shange Tang, Bohan Lyu, et al.*2025 · 84 citations*](https://md.research.elamur.ai/free/en/papers/lin-2025-goedel-prover-frontier-model-open/)
6. [**HOList: An Environment for Machine Learning of Higher-Order Theorem Proving arXiv:1904.03241**Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, et al.*2019 · 78 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2019-holist-environment-machine-learning-higher/)
7. **[ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E](https://md.research.elamur.ai/free/en/papers/chvalovsky-2019-enigma-ng-efficient-neural-gradient/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Karel Chvalovský, Jan Jakubův, Martin Suda, et al.*2019 · 61 citations*
8. [**Learning to Prove Theorems by Learning to Generate Theorems arXiv:2002.07019**Mingzhe Wang, Jia Deng*2020 · 53 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-learning-prove-theorems-learning-generate/)
9. [**Formal Mathematical Reasoning: A New Frontier in AI arXiv:2412.16075**Kaiyu Yang, Gabriel Poesia, Jingxuan He, et al.*2024 · 49 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-formal-mathematical-reasoning-new-frontier/)
10. [**A Survey on Deep Learning for Theorem Proving arXiv:2404.09939**Zhaoyu Li, Jialiang Sun, Logan Murphy, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-survey-deep-learning-theorem-proving/)
11. [**STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving arXiv:2502.00212**Kefan Dong, Tengyu Ma*2025 · 43 citations*](https://md.research.elamur.ai/free/en/papers/dong-2025-stp-self-play-llm-theorem/)
12. [**INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving arXiv:2007.02924**Yuhuai Wu, Albert Qiaochu Jiang, Jimmy Ba, et al.*2020 · 41 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-int-inequality-benchmark-evaluating-generalization/)
13. [**Learning to Reason in Large Theories without Imitation arXiv:1905.10501**Kshitij Bansal, Christian Szegedy, Markus N. Rabe, et al.*2019 · 36 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2019-learning-reason-large-theories-without/)
14. **[Property Invariant Embedding for Automated Reasoning](https://md.research.elamur.ai/free/en/papers/olsak-2019-property-invariant-embedding-automated-reasoning/) [Frontiers in artificial intelligence and applications](https://md.research.elamur.ai/free/en/venues/frontiers-in-artificial-intelligence-and-applications/)**Miroslav Olšák, Cezary Kaliszyk, Josef Urban*2019 · 30 citations*
15. [**TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning arXiv:2102.09756**Minchao Wu, Michael Norrish, Christian Walder, et al.*2021 · 24 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-tacticzero-learning-prove-theorems-scratch/)
16. [**LEAN-GitHub: Compiling GitHub LEAN repositories for a versatile LEAN prover arXiv:2407.17227**Zijian Wu, Jiayu Wang, Dahua Lin, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-lean-github-compiling-github-lean/)
17. **[Learning Formal Mathematics From Intrinsic Motivation](https://md.research.elamur.ai/free/en/papers/poesia-2024-learning-formal-mathematics-intrinsic-motivation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Gabriel Poesia, David Broman, Nick Haber, et al.*2024 · 23 citations*
18. **[Towards Finding Longer Proofs](https://md.research.elamur.ai/free/en/papers/zombori-2019-finding-longer-proofs/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Zsolt Zombori, Adrián Csiszárik, Henryk Michalewski, et al.*2019 · 21 citations*
19. **[Deep Reinforcement Learning for Synthesizing Functions in Higher-Order Logic](https://md.research.elamur.ai/free/en/papers/gauthier-2019-deep-reinforcement-learning-synthesizing-functions/) [EPiC series in computing](https://md.research.elamur.ai/free/en/venues/epic-series-in-computing/)**Thibault Gauthier*2019 · 15 citations*
20. **[Guiding Inferences in Connection Tableau by Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/piotrowski-2019-guiding-inferences-connection-tableau-recurrent/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Bartosz Piotrowski, Josef Urban*2019 · 15 citations*
21. [**Prover Agent: An Agent-Based Framework for Formal Mathematical Proofs arXiv:2506.19923**Kaito Baba, Chaoran Liu, Shuhei Kurita, et al.*2025 · 14 citations*](https://md.research.elamur.ai/free/en/papers/baba-2025-prover-agent-agent-based-framework/)
22. [**Learning to Prove from Synthetic Theorems arXiv:2006.11259**Eser Aygün, Zafarali Ahmed, Ankit Anand, et al.*2020 · 12 citations*](https://md.research.elamur.ai/free/en/papers/aygun-2020-learning-prove-synthetic-theorems/)
23. [**Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning arXiv:1807.08058**Gil Lederman, Markus N. Rabe, Edward A. Lee, et al.*2018 · 12 citations*](https://md.research.elamur.ai/free/en/papers/lederman-2018-learning-heuristics-quantified-boolean-formulas/)
24. [**AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement arXiv:2412.06176**Pranjal Aggarwal, Bryan Parno, Sean Welleck*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/aggarwal-2024-alphaverus-bootstrapping-formally-verified-code/)
25. **[Tandem Training for Language Models](https://md.research.elamur.ai/free/en/papers/west-2026-tandem-training-language-models/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Robert West, Ashton Anderson, Ece Kamar, et al.*2026*

10 more papers of our library cite it; they have no page here yet.

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