Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

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Fields: AI & machine learning

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147 papers of our library cite it (of the 140673 whose bibliographies we read).

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The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2503.09567 · arXiv 2503.09567

@misc{chen2025reasoning,
  title = {{Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models}},
  author = {Chen, Qiguang and Qin, Libo and Liu, Jinhao and Peng, Dengyun and Guan, Jiannan and Wang, Peng and Hu, Mengkang and Zhou, Yuhang and Gao, Te and Che, Wanxiang},
  year = {2025},
  doi = {10.48550/arxiv.2503.09567},
  eprint = {2503.09567},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2503.09567}
}

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1046. Kongcheng Zhang, Qi Yao, Baisheng Lai, Jiaxing Huang, Wenkai Fang, Dacheng Tao, Mingli Song, and Shunyu Liu. Reasoning with reinforced functional token tuning. arXiv preprint arXiv:2502.13389, 2025. — [arXiv:2502.13389](https://arxiv.org/abs/2502.13389) find
1047. Kongcheng Zhang, Qi Yao, Shunyu Liu, Yingjie Wang, Baisheng Lai, Jieping Ye, Mingli Song, and Dacheng Tao. Consistent paths lead to truth: Self-rewarding reinforcement learning for llm reasoning. arXiv preprint arXiv:2506.08745, 2025. — [arXiv:2506.08745](https://arxiv.org/abs/2506.08745) find
1048. Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, and Rishabh Agarwal. Generative verifiers: Reward modeling as next-token prediction. arXiv preprint arXiv:2408.15240, 2024. find our card
1049. Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, et al. Llmeval-med: A real-world clinical benchmark for medical llms with physician validation. arXiv preprint arXiv:2506.04078, 2025. find
1050. Ming-Liang Zhang, Fei yin, and Cheng-Lin Liu. A multi-modal neural geometric solver with textual clauses parsed from diagram. In Edith Elkind, editor, Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pages 3374– 3382. International Joint Conferenc… — [doi:10.24963/ijcai.2023/376](https://doi.org/10.24963/ijcai.2023/376) find
1051. Qingjie Zhang, Han Qiu, Di Wang, Haoting Qian, Yiming Li, Tianwei Zhang, and Minlie Huang. Understanding the dark side of llms’ intrinsic self-correction. arXiv preprint arXiv:2412.14959, 2024. find
1052. Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, Lei Wang, Weixu Zhang, Zhihan Guo, Yufei Wang, Irwin King, Xue Liu, and Chen Ma. What, how, where, and how well? a survey on test-time scaling in large language models. arXiv preprint arXiv:2503.24235, 2025. find
1053. Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, Lei Wang, Weixu Zhang, Wenyue Hua, Haolun Wu, Zhihan Guo, Yufei Wang, Niklas Muennighoff, et al. A survey on test-time scaling in large language models: What, how, where, and how well? arXiv preprint arXiv:2503.24235, 2025. find
1054. Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, et al. Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pages 169–186. Springer, October 2024. URL… find our card
1055. Shaowei Zhang and Deyi Xiong. BackMATH: Towards backward reasoning for solving math problems step by step. In Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, Steven Schockaert, Kareem Darwish, and Apoorv Agarwal, editors, Proceedings of the 31st International Confe… find
1056. Shenao Zhang, Yaqing Wang, Yinxiao Liu, Tianqi Liu, Peter Grabowski, Eugene Ie, Zhaoran Wang, and Yunxuan Li. Beyond markovian: Reflective exploration via bayes-adaptive rl for llm reasoning. arXiv preprint arXiv:2505.20561, 2025. find
1057. Shengjia Zhang, Junjie Wu, Jiawei Chen, Changwang Zhang, Xingyu Lou, Wangchunshu Zhou, Sheng Zhou, Can Wang, and Jun Wang. Othink-r1: Intrinsic fast/slow thinking mode switching for over-reasoning mitigation. arXiv preprint arXiv:2506.02397, 2025. — [arXiv:2506.02397](https://arxiv.org/abs/2506.02397) find
1058. Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, et al. Instruction tuning for large language models: A survey. arXiv preprint arXiv:2308.10792, 2023. find our card
1059. Shimao Zhang, Xiao Liu, Xin Zhang, Junxiao Liu, Zheheng Luo, Shujian Huang, and Yeyun Gong. Process-based self-rewarding language models. arXiv preprint arXiv:2503.03746, 2025. find
1060. Weizhi Zhang, Yangning Li, Yuanchen Bei, Junyu Luo, Guancheng Wan, Liangwei Yang, Chenxuan Xie, Yuyao Yang, Wei-Chieh Huang, Chunyu Miao, et al. From web search towards agentic deep research: Incentivizing search with reasoning agents. arXiv preprint arXiv:2506.18959, 2025. — [arXiv:2506.18959](https://arxiv.org/abs/2506.18959) find
1061. Wenjing Zhang, Xuejiao Lei, Zhaoxiang Liu, Ning Wang, Zhenhong Long, Peijun Yang, Jiaojiao Zhao, Minjie Hua, Chaoyang Ma, Kai Wang, et al. Safety evaluation of deepseek models in chinese contexts. arXiv preprint arXiv:2502.11137, 2025. — [arXiv:2502.11137](https://arxiv.org/abs/2502.11137) find
1062. Wenqi Zhang, Yongliang Shen, Linjuan Wu, Qiuying Peng, Jun Wang, Yueting Zhuang, and Weiming Lu. Self-contrast: Better reflection through inconsistent solving perspectives. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd Annual Meeting of the Association for Comput… find
1063. Xiaoyun Zhang, Jingqing Ruan, Xing Ma, Yawen Zhu, Haodong Zhao, Hao Li, Jiansong Chen, Ke Zeng, and Xunliang Cai. When to continue thinking: Adaptive thinking mode switching for efficient reasoning. arXiv preprint arXiv:2505.15400, 2025. find
1064. Xinyu Zhang, Yuxuan Dong, Yanrui Wu, Jiaxing Huang, Chengyou Jia, Basura Fernando, Mike Zheng Shou, Lingling Zhang, and Jun Liu. Physreason: A comprehensive benchmark towards physics-based reasoning. arXiv preprint arXiv:2502.12054, 2025. find
1065. Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, and Min Lin. Chain of preference optimization: Improving chain-of-thought reasoning in llms. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, editors, Advances in Neural Information Processing Systems, volume 3… find
1066. Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu, and Wanxiang Che. Rot: Enhancing table reasoning with iterative row-wise traversals. arXiv preprint arXiv:2505.15110, 2025. find
1067. Yifan Zhang, Yang Yuan, and Andrew Chi-Chih Yao. On the diagram of thought. arXiv preprint arXiv:2409.10038, 2024. — [arXiv:2409.10038](https://arxiv.org/abs/2409.10038) find
1068. Yifan Zhang, Wenyu Du, Dongming Jin, Jie Fu, and Zhi Jin. Finite state automata inside transformers with chain-of-thought: A mechanistic study on state tracking. arXiv preprint arXiv:2502.20129, 2025. find
1069. Yong Zhang, Bingyuan Zhang, Zhitao Li, Ming Li, Ning Cheng, Minchuan Chen, Tao Wei, Jun Ma, Shaojun Wang, and Jing Xiao. Self-enhanced reasoning training: Activating latent reasoning in small models for enhanced reasoning distillation. arXiv preprint arXiv:2502.12744, 2025. find
1070. Yongheng Zhang, Qiguang Chen, Min Li, Wanxiang Che, and Libo Qin. AutoCAP: Towards automatic cross-lingual alignment planning for zero-shot chain-of-thought. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Findings of the Association for Computational Linguistics: ACL 2024, pages 9191–920… find
1071. Yongheng Zhang, Qiguang Chen, Jingxuan Zhou, Peng Wang, Jiasheng Si, Jin Wang, Wenpeng Lu, and Libo Qin. Wrong-of-thought: An integrated reasoning framework with multi-perspective verification and wrong information. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Findings of the Assoc… find
1072. Yongheng Zhang, Xu Liu, Ruihan Tao, Qiguang Chen, Hao Fei, Wanxiang Che, and Libo Qin. Vitcot: Video-text interleaved chain-of-thought for boosting video understanding in large language models. arXiv preprint arXiv:2507.09876, 2025. — [arXiv:2507.09876](https://arxiv.org/abs/2507.09876) find
1073. Yongheng Zhang, Xu Liu, Ruoxi Zhou, Qiguang Chen, Hao Fei, Wenpeng Lu, and Libo Qin. Cchall: A novel benchmark for joint cross-lingual and cross-modal hallucinations detection in large language models. arXiv preprint arXiv:2505.19108, 2025. find
1074. Yudi Zhang, Lu Wang, Meng Fang, Yali Du, Chenghua Huang, Jun Wang, Qingwei Lin, Mykola Pechenizkiy, Dongmei Zhang, Saravan Rajmohan, et al. Distill not only data but also rewards: Can smaller language models surpass larger ones? arXiv preprint arXiv:2502.19557, 2025. — [arXiv:2502.19557](https://arxiv.org/abs/2502.19557) find
1075. Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim, Moontae Lee, Honglak Lee, and Lu Wang. Small language models need strong verifiers to self-correct reasoning. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Findings of the Association for Computational Linguistics: AC… find
1076. Yuxiang Zhang, Shangxi Wu, Yuqi Yang, Jiangming Shu, Jinlin Xiao, Chao Kong, and Jitao Sang. o1-coder: an o1 replication for coding. arXiv preprint arXiv:2412.00154, 2024. — [arXiv:2412.00154](https://arxiv.org/abs/2412.00154) find
1077. Yuxiang Zhang, Yuqi Yang, Jiangming Shu, Yuhang Wang, Jinlin Xiao, and Jitao Sang. Openrft: Adapting reasoning foundation model for domain-specific tasks with reinforcement fine-tuning. arXiv preprint arXiv:2412.16849, 2024. — [arXiv:2412.16849](https://arxiv.org/abs/2412.16849) find
1078. Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. The lessons of developing process reward models in mathematical reasoning. arXiv preprint arXiv:2501.07301, 2025. find our card
1079. Zhihao Zhang, Qiaole Dong, Qi Zhang, Jun Zhao, Enyu Zhou, Zhiheng Xi, Senjie Jin, Xiaoran Fan, Yuhao Zhou, Yanwei Fu, et al. Reinforcement fine-tuning enables mllms learning novel tasks stably. arXiv preprint arXiv:2506.23508, 2025. — [arXiv:2506.23508](https://arxiv.org/abs/2506.23508) find
1080. Zhongwang Zhang, Pengxiao Lin, Zhiwei Wang, Yaoyu Zhang, and Zhi-Qin John Xu. Complexity control facilitates reasoning-based compositional generalization in transformers. arXiv preprint arXiv:2501.08537, 2025. — [arXiv:2501.08537](https://arxiv.org/abs/2501.08537) find
1081. Zhuosheng Zhang, Aston Zhang, Mu Li, hai zhao, George Karypis, and Alex Smola. Multimodal chain-of-thought reasoning in language models. Transactions on Machine Learning Research, June 2024. ISSN 2835-8856. URL https://openreview.net/forum? id=y1pPWFVfvR. ↗ our card
1082. Deji Zhao, Donghong Han, Jia Wu, Zhongjiang He, Bo Ning, Ye Yuan, Yongxiang Li, Chao Wang, and Shuangyong Song. Enhancing math reasoning ability of large language models via computation logic graphs. Knowledge-Based Systems, page 113905, 2025. find
1083. Eric Zhao, Pranjal Awasthi, and Sreenivas Gollapudi. Sample, scrutinize and scale: Effective inference-time search by scaling verification. arXiv preprint arXiv:2502.01839, 2025. — [arXiv:2502.01839](https://arxiv.org/abs/2502.01839) find
1084. Han Zhao, Haotian Wang, Yiping Peng, Sitong Zhao, Xiaoyu Tian, Shuaiting Chen, Yunjie Ji, and Xiangang Li. 1.4 million open-source distilled reasoning dataset to empower large language model training. arXiv preprint arXiv:2503.19633, 2025. — [arXiv:2503.19633](https://arxiv.org/abs/2503.19633) find
1085. Jun Zhao, Jingqi Tong, Yurong Mou, Ming Zhang, Qi Zhang, and Xuanjing Huang. Exploring the compositional deficiency of large language models in mathematical reasoning through trap problems. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Proceedings of the 2024 Conference on Empirical… find
1086. Lili Zhao, Yang Wang, Qi Liu, Mengyun Wang, Wei Chen, Zhichao Sheng, and Shijin Wang. Evaluating large language models through role-guide and self-reflection: A comparative study. In The Thirteenth International Conference on Learning Representations, January 2025. URL https://openreview.net/forum?i… ↗
1087. Shangziqi Zhao, Jiahao Yuan, Guisong Yang, and Usman Naseem. Can pruning improve reasoning? revisiting long-cot compression with capability in mind for better reasoning. arXiv preprint arXiv:2505.14582, 2025. — [arXiv:2505.14582](https://arxiv.org/abs/2505.14582) find
1088. Weixiang Zhao, Jiahe Guo, Yang Deng, Xingyu Sui, Yulin Hu, Yanyan Zhao, Wanxiang Che, Bing Qin, Tat-Seng Chua, and Ting Liu. Exploring and exploiting the inherent efficiency within large reasoning models for self-guided efficiency enhancement. arXiv preprint arXiv:2506.15647, 2025. — [arXiv:2506.15647](https://arxiv.org/abs/2506.15647) find
1089. Xuandong Zhao, Zhewei Kang, Aosong Feng, Sergey Levine, and Dawn Song. Learning to reason without external rewards. arXiv preprint arXiv:2505.19590, 2025. find
1090. Xueliang Zhao, Wei Wu, Jian Guan, and Lingpeng Kong. Promptcot: Synthesizing olympiad-level problems for mathematical reasoning in large language models. arXiv preprint arXiv:2503.02324, 2025. find
1091. Xufeng Zhao, Mengdi Li, Wenhao Lu, Cornelius Weber, Jae Hee Lee, Kun Chu, and Stefan Wermter. Enhancing zero-shot chain-of-thought reasoning in large language models through logic. In Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, and Nianwen Xue, editors, Proce… find
1092. Yachao Zhao, Bo Wang, and Yan Wang. Explicit vs. implicit: Investigating social bias in large language models through self-reflection. arXiv preprint arXiv:2501.02295, 2025. find
1093. Yang Zhao, Kai Xiong, Xiao Ding, Li Du, Zhouhao Sun, Jiannan Guan, Wenbin Zhang, Bin Liu, Dong Hu, Bing Qin, et al. Ufo-rl: Uncertainty-focused optimization for efficient reinforcement learning data selection. arXiv preprint arXiv:2505.12457, 2025. — [arXiv:2505.12457](https://arxiv.org/abs/2505.12457) find
1094. Yichong Zhao and Susumu Goto. Can frontier llms replace annotators in biomedical text mining? analyzing challenges and exploring solutions. arXiv preprint arXiv:2503.03261, 2025. — [arXiv:2503.03261](https://arxiv.org/abs/2503.03261) find
1095. Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang, Tianqi Shi, Chenyang Lyu, Longyue Wang, Weihua Luo, and Kaifu Zhang. Marco-o1: Towards open reasoning models for open-ended solutions. arXiv preprint arXiv:2411.14405, 2024. find
1096. Yurui Zhao, Xiang Wang, Jiahong Liu, Irwin King, and Zhitao Huang. Towards geometry problem solving in the large model era: A survey. arXiv preprint arXiv:2506.02690, 2025. — [arXiv:2506.02690](https://arxiv.org/abs/2506.02690) find
1097. Zhonghan Zhao, Wenwei Zhang, Haian Huang, Kuikun Liu, Jianfei Gao, Gaoang Wang, and Kai Chen. Rig: Synergizing reasoning and imagination in end-to-end generalist policy. arXiv preprint arXiv:2503.24388, 2025. — [arXiv:2503.24388](https://arxiv.org/abs/2503.24388) find
1098. Zilong Zhao, Yao Rong, Dongyang Guo, Emek Gözlüklü, Emir Gülboy, and Enkelejda Kasneci. Stepwise self-consistent mathematical reasoning with large language models. arXiv preprint arXiv:2402.17786, 2024. — [arXiv:2402.17786](https://arxiv.org/abs/2402.17786) find
1099. Zirui Zhao, Wee Sun Lee, and David Hsu. Large language models as commonsense knowledge for large-scale task planning. Advances in Neural Information Processing Systems, 36:31967–31987, December 2023. URL https://openreview.net/pdf?id= tED747HURfX. ↗
1100. Bowen Zheng, Xiaolei Wang, Enze Liu, Xi Wang, Lu Hongyu, Yu Chen, Wayne Xin Zhao, and Ji-Rong Wen. Deeprec: Towards a deep dive into the item space with large language model based recommendation. arXiv preprint arXiv:2505.16810, 2025. — [arXiv:2505.16810](https://arxiv.org/abs/2505.16810) find
1101. Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li. Progressive-hint prompting improves reasoning in large language models. In AI for Math Workshop @ ICML 2024, June 2024. URL https://openreview.net/forum?id=UkFEs3ciz8. ↗
1102. Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. Processbench: Identifying process errors in mathematical reasoning. arXiv preprint arXiv:2412.06559, 2024. find our card
1103. Da Zheng, Lun Du, Junwei Su, Yuchen Tian, Yuqi Zhu, Jintian Zhang, Lanning Wei, Ningyu Zhang, and Huajun Chen. Knowledge augmented complex problem solving with large language models: A survey. arXiv preprint arXiv:2505.03418, 2025. — [arXiv:2505.03418](https://arxiv.org/abs/2505.03418) find
1104. Ge Zheng, Bin Yang, Jiajin Tang, Hong-Yu Zhou, and Sibei Yang. Ddcot: Duty-distinct chain-of-thought prompting for multimodal reasoning in language models. Advances in Neural Information Processing Systems, 36:5168–5191, 2023. find
1105. Hang Zheng, Hongshen Xu, Yuncong Liu, Lu Chen, Pascale Fung, and Kai Yu. Enhancing llm reliability via explicit knowledge boundary modeling. arXiv preprint arXiv:2503.02233, 2025. — [arXiv:2503.02233](https://arxiv.org/abs/2503.02233) find
1106. Jiani Zheng, Lu Wang, Fangkai Yang, Chaoyun Zhang, Lingrui Mei, Wenjie Yin, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, and Qi Zhang. Vem: Environment-free exploration for training gui agent with value environment model. arXiv preprint arXiv:2502.18906, 2025. — [arXiv:2502.18906](https://arxiv.org/abs/2502.18906) find
1107. Kunhao Zheng, Juliette Decugis, Jonas Gehring, Taco Cohen, benjamin negrevergne, and Gabriel Synnaeve. What makes large language models reason in (multi-turn) code generation? In The Thirteenth International Conference on Learning Representations, January 2025. URL https://openreview.net/forum?id=Zk… ↗
1108. Tianyu Zheng, Ge Zhang, Tianhao Shen, Xueling Liu, Bill Yuchen Lin, Jie Fu, Wenhu Chen, and Xiang Yue. OpenCodeInterpreter: Integrating code generation with execution and refinement. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Findings of the Association for Computational Linguistics:… find
1109. Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie Lu, Xianpei Han, Debing Zhang, and Le Sun. Critic-cot: Boosting the reasoning abilities of large language model via chain-of-thoughts critic. arXiv preprint arXiv:2408.16326, 2024. — [arXiv:2408.16326](https://arxiv.org/abs/2408.16326) find
1110. Zhi Zheng, Zhuoliang Xie, Zhenkun Wang, and Bryan Hooi. Monte carlo tree search for comprehensive exploration in llm-based automatic heuristic design. arXiv preprint arXiv:2501.08603, 2025. — [arXiv:2501.08603](https://arxiv.org/abs/2501.08603) find
1111. Jianyuan Zhong, Zeju Li, Zhijian Xu, Xiangyu Wen, and Qiang Xu. Dyve: Thinking fast and slow for dynamic process verification. arXiv preprint arXiv:2502.11157, 2025. find
1112. Qihuang Zhong, Kang Wang, Ziyang Xu, Juhua Liu, Liang Ding, and Bo Du. Achieving\> 97% on gsm8k: Deeply understanding the problems makes llms better solvers for math word problems. arXiv preprint arXiv:2404.14963, 2024. — [arXiv:2404.14963](https://arxiv.org/abs/2404.14963) find
1113. Tianyang Zhong, Zhengliang Liu, Yi Pan, Yutong Zhang, Yifan Zhou, Shizhe Liang, Zihao Wu, Yanjun Lyu, Peng Shu, Xiaowei Yu, et al. Evaluation of openai o1: Opportunities and challenges of agi. arXiv preprint arXiv:2409.18486, 2024. find
1114. Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. Language agent tree search unifies reasoning, acting, and planning in language models. In Forty-first International Conference on Machine Learning, May 2024. URL https: //openreview.net/forum?id=njwv9BsGHF. find
1115. Aojun Zhou, Ke Wang, Zimu Lu, Weikang Shi, Sichun Luo, Zipeng Qin, Shaoqing Lu, Anya Jia, Linqi Song, Mingjie Zhan, and Hongsheng Li. Solving challenging math word problems using GPT-4 code interpreter with code-based self-verification. In The Twelfth International Conference on Learning Representat… find our card
1116. Changzhi Zhou, Xinyu Zhang, Dandan Song, Xiancai Chen, Wanli Gu, Huipeng Ma, Yuhang Tian, Mengdi Zhang, and Linmei Hu. Refinecoder: Iterative improving of large language models via adaptive critique refinement for code generation. arXiv preprint arXiv:2502.09183, 2025. — [arXiv:2502.09183](https://arxiv.org/abs/2502.09183) find
1117. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. Least-to-most prompting enables complex reasoning in large language models. In The Eleventh International Conference on Learning Representations, Feb… find our card
1118. Fan Zhou, Haoyu Dong, Qian Liu, Zhoujun Cheng, Shi Han, and Dongmei Zhang. Reflection of thought: Inversely eliciting numerical reasoning in language models via solving linear systems. arXiv preprint arXiv:2210.05075, 2022. — [arXiv:2210.05075](https://arxiv.org/abs/2210.05075) find
1119. Hengguang Zhou, Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, and Cho-Jui Hsieh. R1-zero’s" aha moment" in visual reasoning on a 2b non-sft model. arXiv preprint arXiv:2503.05132, 2025. — [arXiv:2503.05132](https://arxiv.org/abs/2503.05132) find
1120. Jin Peng Zhou, Charles E Staats, Wenda Li, Christian Szegedy, Kilian Q Weinberger, and Yuhuai Wu. Don’t trust: Verify – grounding LLM quantitative reasoning with autoformalization. In The Twelfth International Conference on Learning Representations, January 2024. URL https://openreview.net/forum?id=… ↗
1121. Jin Peng Zhou, Kaiwen Wang, Jonathan Chang, Zhaolin Gao, Nathan Kallus, Kilian Q Weinberger, Kianté Brantley, and Wen Sun. q♯: Provably optimal distributional rl for llm post-training. arXiv preprint arXiv:2502.20548, 2025. — [arXiv:2502.20548](https://arxiv.org/abs/2502.20548) find
1122. Kaiwen Zhou, Chengzhi Liu, Xuandong Zhao, Shreedhar Jangam, Jayanth Srinivasa, Gaowen Liu, Dawn Song, and Xin Eric Wang. The hidden risks of large reasoning models: A safety assessment of r1. arXiv preprint arXiv:2502.12659, 2025. find
1123. Lexin Zhou, Wout Schellaert, Fernando Martínez-Plumed, Yael Moros-Daval, Cèsar Ferri, and José Hernández-Orallo. Larger and more instructable language models become less reliable. Nature, 634(8032):61–68, 2024. URL https://www.nature.com/articles/ s41586-024-07930-y. ↗
1124. Li Zhou, Ruijie Zhang, Xunlian Dai, Daniel Hershcovich, and Haizhou Li. Large language models penetration in scholarly writing and peer review. arXiv preprint arXiv:2502.11193, 2025. — [arXiv:2502.11193](https://arxiv.org/abs/2502.11193) find
1125. Ruochen Zhou, Minrui Xu, Shiqi Chen, Junteng Liu, Yunqi Li, LIN Xinxin, Zhengyu Chen, and Junxian He. AI for math or math for AI? on the generalization of learning mathematical problem solving. In The 4th Workshop on Mathematical Reasoning and AI at NeurIPS’24, 2024. URL https://openreview.net/forum… ↗
1126. Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig. Webarena: A realistic web environment for building autonomous agents. In The Twelfth International Conference on Learning Representations, Jan… find our card
1127. Xiangxin Zhou, Zichen Liu, Anya Sims, Haonan Wang, Tianyu Pang, Chongxuan Li, Liang Wang, Min Lin, and Chao Du. Reinforcing general reasoning without verifiers. arXiv preprint arXiv:2505.21493, 2025. — [arXiv:2505.21493](https://arxiv.org/abs/2505.21493) find
1128. Xiaofeng Zhou, Heyan Huang, and Lizi Liao. Debate, reflect, and distill: Multi-agent feedback with tree-structured preference optimization for efficient language model enhancement. arXiv preprint arXiv:2506.03541, 2025. find
1129. Xin Zhou, Yiwen Guo, Ruotian Ma, Tao Gui, Qi Zhang, and Xuanjing Huang. Self-consistency of the internal reward models improves self-rewarding language models. arXiv preprint arXiv:2502.08922, 2025. — [arXiv:2502.08922](https://arxiv.org/abs/2502.08922) find
1130. Yang Zhou, Hongyi Liu, Zhuoming Chen, Yuandong Tian, and Beidi Chen. Gsm-infinite: How do your llms behave over infinitely increasing context length and reasoning complexity? arXiv preprint arXiv:2502.05252, 2025. find
1131. Yifei Zhou, Song Jiang, Yuandong Tian, Jason Weston, Sergey Levine, Sainbayar Sukhbaatar, and Xian Li. Sweet-rl: Training multi-turn llm agents on collaborative reasoning tasks. arXiv preprint arXiv:2503.15478, 2025. — [arXiv:2503.15478](https://arxiv.org/abs/2503.15478) find
1132. Yufa Zhou, Shaobo Wang, Xingyu Dong, Xiangqi Jin, Yifang Chen, Yue Min, Kexin Yang, Xingzhang Ren, Dayiheng Liu, and Linfeng Zhang. Reasoning like an economist: Post-training on economic problems induces strategic generalization in llms. arXiv preprint arXiv:2506.00577, 2025. — [arXiv:2506.00577](https://arxiv.org/abs/2506.00577) find
1133. Zhanke Zhou, Zhaocheng Zhu, Xuan Li, Mikhail Galkin, Xiao Feng, Sanmi Koyejo, Jian Tang, and Bo Han. Landscape of thoughts: Visualizing the reasoning process of large language models. arXiv preprint arXiv:2503.22165, 2025. — [arXiv:2503.22165](https://arxiv.org/abs/2503.22165) find
1134. Zhi Zhou, Tan Yuhao, Zenan Li, Yuan Yao, Lan-Zhe Guo, Xiaoxing Ma, and Yu-Feng Li. Bridging internal probability and self-consistency for effective and efficient llm reasoning. arXiv preprint arXiv:2502.00511, 2025. — [arXiv:2502.00511](https://arxiv.org/abs/2502.00511) find
1135. Bin Zhu, Hailong Yin, Jingjing Chen, and Yu-Gang Jiang. Reasoning models are more easily gaslighted than you think. arXiv preprint arXiv:2506.09677, 2025. — [arXiv:2506.09677](https://arxiv.org/abs/2506.09677) find
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