🧑‍🔬 Who cites Tian Ye

84 citations of their works from 84 papers of our library (of the 149,439 whose bibliographies we read) · 34 with a page here, the most cited first · ← Tian Ye

1. DeepSeek-V3.2: Pushing the Frontier of Open Large Language ModelsarXiv:2512.02556DeepSeek-AI, Aixin Liu, Aoxue Mei, et al.2025 · 341 citations
2. GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language ModelsarXiv:2410.05229Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, et al.2024 · 273 citations
3. Large Language Diffusion ModelsConference on Neural Information Processing Systems (NeurIPS)Shen Nie, Fengqi Zhu, Zebin You, et al.2025 · 178 citations
4. SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-trainingarXiv:2501.17161Tianzhe Chu, Yuexiang Zhai, Jihan Yang, et al.2025 · 160 citations
5. DeepSeek-V4: Towards Highly Efficient Million-Token Context IntelligencearXiv:2606.19348DeepSeek-AI, Anyi Xu, Bangcai Lin, et al.2026 · 122 citations
6. Physics of Language Models: Part 3.1, Knowledge Storage and ExtractionarXiv:2309.14316Zeyuan Allen-Zhu, Yuanzhi Li2023 · 116 citations
7. Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement LearningarXiv:2502.14768Tian Xie, Zitian Gao, Qingnan Ren, et al.2025 · 104 citations
8. When More is Less: Understanding Chain-of-Thought Length in LLMsarXiv:2502.07266Yuyang Wu, Yifei Wang, Ziyu Ye, et al.2025 · 73 citations
9. Physics of Language Models: Part 3.2, Knowledge ManipulationarXiv:2309.14402Zeyuan Allen-Zhu, Yuanzhi Li2023 · 72 citations
10. On Memorization of Large Language Models in Logical ReasoningInternational Joint Conference on Natural Language Processing (IJCNLP)Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.2025 · 45 citations
11. On the Diversity of Synthetic Data and its Impact on Training Large Language ModelsarXiv:2410.15226Hao Chen, Abdul Waheed, Xiang Li, et al.2024 · 35 citations
12. How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled BenchmarkMinglai Yang, Ethan Huang, Liang Zhang, et al.2025 · 25 citations
13. Scaling Latent Reasoning via Looped Language ModelsarXiv:2510.25741Rui-Jie Zhu, Zixuan Wang, Kai Hua, et al.2025 · 22 citations
14. GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity?arXiv:2502.05252Yang Zhou, Hongyi Liu, Zhuoming Chen, et al.2025 · 21 citations
15. Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from ExperiencearXiv:2512.17260Jiangjie Chen, Wenxiang Chen, Jiacheng Du, et al.2025 · 19 citations
16. Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy DistillationarXiv:2603.19220Zhuolin Yang, Zihan Liu, Yang Chen, et al.2026 · 16 citations
17. Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language ModelsarXiv:2503.24377Rui Wang, Hongru Wang, Boyang Xue, et al.2025 · 16 citations
18. What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient PerspectiveAnnual Meeting of the Association for Computational Linguistics (ACL)Ming Li, Yanhong Li, Tianyi Zhou2025 · 15 citations
19. Do LLMs Really Think Step-by-step In Implicit Reasoning?arXiv:2411.15862Yijiong Yu2024 · 10 citations
20. Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language ModelsFindings of the Association for Computational LinguisticsHengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.2026 · 9 citations
21. MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoyuan Li, Keqin Bao, Yubo Ma, et al.2026 · 9 citations
22. Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUsarXiv:2504.07866Yichun Yin, Wenyong Huang, Kaikai Song, et al.2025 · 6 citations
23. Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Hao Wang, Hao Gu, Hongming Piao, et al.2026 · 3 citations
24. Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Binghai Wang, Yantao Liu, Yuxuan Liu, et al.2026 · 3 citations
25. Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier MathAnnual Meeting of the Association for Computational Linguistics (ACL)Shrey Pandit, Austin Xu, Xuan-Phi Nguyen, et al.2026 · 2 citations
26. Streaming Hallucination Detection in Long Chain-of-Thought ReasoningFindings of the Association for Computational LinguisticsHaolang Lu, Minghui Pan, Ripeng LI, et al.2026 · 1 citation
27. AgentV-RL: Scaling Reward Modeling with Agentic VerifierFindings of the Association for Computational LinguisticsJiazheng Zhang, Ziche Fu, Zhiheng Xi, et al.2026
28. Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric RewardsAnnual Meeting of the Association for Computational Linguistics (ACL)Jiajie Zhang, Xin Lv, Ling Feng, et al.2026
29. Curing Miracle Steps in LLM Mathematical Reasoning with Rubric RewardsAnnual Meeting of the Association for Computational Linguistics (ACL)Youliang Yuan, Qiuyang Mang, Jingbang Chen, et al.2026
30. 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
31. Fico: Evaluating Vision-Language Models under Visual Fidelity and Compression at ScaleFindings of the Association for Computational LinguisticsJianhong Tu, Nicholas Crispino, Kyle Montgomery, et al.2026
32. ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction FollowingAnnual Meeting of the Association for Computational Linguistics (ACL)Yuancheng Yang, Lin Yang, Xu Wang, et al.2026
33. RADO: Reasoning Audit-Driven Optimization for Rigorous Reasoning in High-Stakes DomainsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhijie Tan, Xu Chu, Guanyu Wang, et al.2026
34. Tailored Primitive Initialization is the Secret Key to Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Yihang Yao, Guangtao Zeng, Raina Wu, et al.2026

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