🔗 Who cites «LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!»

56 papers of our library cite it (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · ← back to «LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!»

1. 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
2. Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM ReasoningConference on Neural Information Processing Systems (NeurIPS)Shenzhi Wang, Le Yu, Chang Gao, et al.2025 · 152 citations
3. Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRsarXiv:2503.01307Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, et al.2025 · 123 citations
4. The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem ComplexityConference on Neural Information Processing Systems (NeurIPS)Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, et al.2025 · 104 citations
5. A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?arXiv:2503.24235Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.2025 · 79 citations
6. A Comprehensive Survey on Long Context Language ModelingarXiv:2503.17407Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.2025 · 64 citations
7. Reasoning Models Can Be Effective Without ThinkingarXiv:2504.09858Wenjie Ma, Jingxuan He, Charlie Snell, et al.2025 · 58 citations
8. A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and BeyondarXiv:2503.21614Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.2025 · 55 citations
9. Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language ModelsarXiv:2502.17387Alon Albalak, Duy Phung, Nathan Lile, et al.2025 · 43 citations
10. KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for CodingFindings of the Association for Computational LinguisticsZhangchen Xu, Yang Liu, Yueqin Yin, et al.2025 · 36 citations
11. OpenCodeReasoning: Advancing Data Distillation for Competitive CodingarXiv:2504.01943Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, et al.2025 · 33 citations
12. Rethinking Reflection in Pre-TrainingarXiv:2504.04022Essential AI: Darsh J Shah, Peter Rushton, Somanshu Singla, et al.2025 · 15 citations
13. Deconstructing Long Chain-of-Thought: A Structured Reasoning Optimization Framework for Long CoT DistillationarXiv:2503.16385Yijia Luo, Yulin Song, Xingyao Zhang, et al.2025 · 14 citations
14. What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Gangwei Jiang, Yahui Liu, Zhaoyi Li, et al.2025 · 11 citations
15. Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative AlignmentAnnual Meeting of the Association for Computational Linguistics (ACL)Yuming Yang, Mingyoung Lai, Wanxu Zhao, et al.2026 · 4 citations
16. Logit Arithmetic Elicits Long Reasoning Capabilities Without TrainingFindings of the Association for Computational LinguisticsYunxiang Zhang, Muhammad Khalifa, Lechen Zhang, et al.2026 · 2 citations
17. Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yongjiang Liu, Haoxi Li, Xiaosong Ma, et al.2026 · 2 citations
18. Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-ReflectionFindings of the Association for Computational LinguisticsZhuoyang Wu, Xinze Li, Zhenghao Liu, et al.2026 · 1 citation
19. How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and ActivationFindings of the Association for Computational LinguisticsHao Yang, Qinghua Zhao, Lei Li, et al.2026 · 1 citation
20. Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model FactualityFindings of the Association for Computational LinguisticsMike Zhang, Johannes Bjerva, Russa Biswas2026
21. Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement LearningFindings of the Association for Computational LinguisticsTiehua Mei, Minxuan Lv, Leiyu Pan, et al.2026
22. Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain GeneralizationAnnual Meeting of the Association for Computational Linguistics (ACL)Tian Xueyun, MingHua Ma, Bingbing Xu, et al.2026
23. MARD: Module-Aware Reasoning Distillation for Language Models with Adaptive SupervisionAnnual Meeting of the Association for Computational Linguistics (ACL)Wenqi Yang, Jianjun Li, Zhibo Zhang, et al.2026

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