🧑‍🔬 Who cites Shaoting Zhang

69 citations of their works from 68 papers of our library (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · ← Shaoting Zhang

1. s1: Simple test-time scalingConference on Empirical Methods in Natural Language Processing (EMNLP)Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.2025 · 608 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. Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language ModelsarXiv:2410.07985Bofei Gao, Feifan Song, Zhe Yang, et al.2024 · 163 citations
4. RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and RefinementConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Jinhao Jiang, Jiayi Chen, Junyi Li, et al.2025 · 31 citations
5. Unlocking Exploration in RLVR: Uncertainty-aware Advantage Shaping for Deeper ReasoningFindings of the Association for Computational LinguisticsCan Xie, Ruotong Pan, Xiangyu Wu, et al.2026 · 15 citations
6. Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging TasksAnnual Meeting of the Association for Computational Linguistics (ACL)Xingxuan Li, Weiwen Xu, Ruochen Zhao, et al.2025 · 13 citations
7. A Survey of Inductive Reasoning for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Kedi Chen, Dezhao Ruan, Yuhao Dan, et al.2026 · 10 citations
8. Interleaved Latent Visual Reasoning with Selective Perceptual ModelingAnnual Meeting of the Association for Computational Linguistics (ACL)Shuai Dong, Siyuan Wang, Xingyu Liu, et al.2026 · 5 citations
9. Strong Reasoning Isn't Enough: Evaluating Evidence Elicitation in Interactive DiagnosisFindings of the Association for Computational LinguisticsZhuohan Long, Zhongyu Wei2026 · 1 citation
10. Towards Stable and Effective Reinforcement Learning for Mixture-of-ExpertsAnnual Meeting of the Association for Computational Linguistics (ACL)Di Zhang, Xun Wu, Shaohan Huang, et al.2026 · 1 citation
11. AlgBench: To What Extent Do Large Reasoning Models Understand Algorithms?Findings of the Association for Computational LinguisticsHenan Sun, Kaichi Yu, Yuyao Wang, et al.2026
12. Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Shaoning Sun, Mingzhu Cai, Huang He, et al.2026
13. Evaluating the Pre-Consultation Ability of LLMs using Diagnostic GuidelinesConference of the European Chapter of the Association for Computational Linguistics (EACL)Jean Seo, Gibaeg Kim, Kihun Shin, et al.2026
14. Improved Policy Optimization for Mixture-of-Experts Models: Importance Sampling and Rewarding from an Expert-Centric PerspectiveFindings of the Association for Computational LinguisticsYining Qian, Jinpeng Li, Fei Mi, et al.2026
15. Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-trainingAnnual Meeting of the Association for Computational Linguistics (ACL)Lei Liu, Hao Zhu, Xiaoyan Yang, et al.2026
16. RIFT: Repurposing Negative Samples via Reward-Informed Fine-TuningFindings of the Association for Computational LinguisticsZehua Liu, Shuqi Liu, Tao Zhong, et al.2026
17. SciPedia: Unlocking the Value of Scientific Data for Pre-trainingAnnual Meeting of the Association for Computational Linguistics (ACL)Yiwei Qin, Zhen Huang, Tiantian Mi, et al.2026

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