🔗 Who cites «Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision»

37 papers of our library cite it (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · ← back to «Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision»

1. WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-InstructarXiv:2308.09583Haipeng Luo, Qingfeng Sun, Can Xu, et al.2023 · 372 citations
2. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language ModelsScience China Information SciencesQiguang Chen, Libo Qin, Jinhao Liu, et al.2025 · 176 citations
3. Improve Mathematical Reasoning in Language Models by Automated Process SupervisionarXiv:2406.06592Liangchen Luo, Yinxiao Liu, Rosanne Liu, et al.2024 · 168 citations
4. Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language ModelsarXiv:2501.09686Fengli Xu, Qianyue Hao, Zefang Zong, et al.2025 · 120 citations
5. Demystifying Long Chain-of-Thought Reasoning in LLMsarXiv:2502.03373Edward Yeo, Yuxuan Tong, Morry Niu, et al.2025 · 119 citations
6. Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionConference on Neural Information Processing Systems (NeurIPS)Zhiqing Sun, Longhui Yu, Yikang Shen, et al.2024 · 51 citations
7. OpenR: An Open Source Framework for Advanced Reasoning with Large Language ModelsarXiv:2410.09671Jun Wang, Meng Fang, Ziyu Wan, et al.2024 · 50 citations
8. A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic SystemsarXiv:2504.09037Zixuan Ke, Fangkai Jiao, Yifei Ming, et al.2025 · 39 citations
9. Evolving Deeper LLM ThinkingarXiv:2501.09891Kuang-Huei Lee, Ian Fischer, Yueh-Hua Wu, et al.2025 · 37 citations
10. Process-Driven Autoformalization in Lean 4arXiv:2406.01940Jianqiao Lu, Yingjia Wan, Zhengying Liu, et al.2024 · 33 citations
11. AutoPSV: Automated Process-Supervised VerifierConference on Neural Information Processing Systems (NeurIPS)Jianqiao Lu, Zhiyang Dou, Hongru Wang, et al.2024 · 28 citations
12. Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM ReasoningarXiv:2507.00432Maggie Huan, Yuetai Li, Tuney Zheng, et al.2025 · 25 citations
13. ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process RewardingInternational ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)Zhongxiang Sun, Qipeng Wang, Weijie Yu, et al.2025 · 22 citations
14. CodePRM: Execution Feedback-enhanced Process Reward Model for Code GenerationFindings of the Association for Computational LinguisticsQingyao Li, Xinyi Dai, Xiangyang Li, et al.2025 · 15 citations
15. Understanding Chain-of-Thought in LLMs through Information TheoryarXiv:2411.11984Jean-Francois Ton, Muhammad Faaiz Taufiq, Yang Liu2024 · 14 citations
16. Process Supervision-Guided Policy Optimization for Code GenerationarXiv:2410.17621Ning Dai, Zheng Wu, Renjie Zheng, et al.2024 · 12 citations
17. An Efficient and Precise Training Data Construction Framework for Process-supervised Reward Model in Mathematical ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Wei Sun, Qianlong Du, Fuwei Cui, et al.2025 · 10 citations
18. FastMCTS: A Simple Sampling Strategy for Data SynthesisAnnual Meeting of the Association for Computational Linguistics (ACL)Peiji Li, Kai Lv, Yunfan Shao, et al.2025 · 8 citations
19. A Survey on Feedback-based Multi-step Reasoning for Large Language Models on MathematicsarXiv:2502.14333Ting-Ruen Wei, Haowei Liu, Xuyang Wu, et al.2025 · 7 citations
20. The Bidirectional Process Reward ModelAnnual Meeting of the Association for Computational Linguistics (ACL)Lingyin Zhang, Jun Gao, Xiaoxue Ren, et al.2026 · 1 citation
21. Time-for-Accuracy: Formalizing Chain-of-Thought as an Expansion of Logical DepthFindings of the Association for Computational LinguisticsYue Wang, Zhi Zhang, Wang Xi, et al.2026

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

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