---
title: "Who cites C3oT: Generating Shorter — 55 citing papers"
description: "55 papers of the Elamur Research library cite «C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness», found in their…"
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# Who cites «C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness»

55 papers of our library cite it (of the 149,439 whose bibliographies we read) · 28 with a page here, the most cited first · [← back to «C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness»](https://md.research.elamur.ai/free/en/papers/kang-2024-c3ot-generating-shorter-chain-thought/)

1. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
2. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models arXiv:2503.16419**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 157 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/)
3. [**L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning arXiv:2503.04697**Pranjal Aggarwal, Sean Welleck*2025 · 125 citations*](https://md.research.elamur.ai/free/en/papers/aggarwal-2025-l1-controlling-long-reasoning-model/)
4. **[TokenSkip: Controllable Chain-of-Thought Compression in LLMs](https://md.research.elamur.ai/free/en/papers/xia-2025-tokenskip-controllable-chain-thought-compression/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Heming Xia, Chak Tou Leong, Wenjie Wang, et al.*2025 · 106 citations*
5. [**Dynamic Early Exit in Reasoning Models arXiv:2504.15895**Chenxu Yang, Qingyi Si, Yongjie Duan, et al.*2025 · 74 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-dynamic-early-exit-reasoning-models/)
6. **[Training Language Models to Reason Efficiently](https://md.research.elamur.ai/free/en/papers/arora-2025-training-language-models-reason-efficiently/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Daman Arora, Andrea Zanette*2025 · 74 citations*
7. **[CoT-Valve: Length-Compressible Chain-of-Thought Tuning](https://md.research.elamur.ai/free/en/papers/ma-2025-cot-valve-length-compressible-chain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyin Ma, Guangnian Wan, Runpeng Yu, et al.*2025 · 64 citations*
8. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/)
9. **[LightThinker: Thinking Step-by-Step Compression](https://md.research.elamur.ai/free/en/papers/zhang-2025-lightthinker-thinking-step-step-compression/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jintian Zhang, Yuqi Zhu, Mengshu Sun, et al.*2025 · 50 citations*
10. [**Efficient Reasoning Models: A Survey arXiv:2504.10903**Sicheng Feng, Gongfan Fang, Xinyin Ma, et al.*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/feng-2025-efficient-reasoning-models-survey/)
11. [**InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models arXiv:2503.06692**Yuchen Yan, Yongliang Shen, Yang Liu, et al.*2025 · 29 citations*](https://md.research.elamur.ai/free/en/papers/yan-2025-inftythink-breaking-length-limits-long/)
12. **[FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/jiang-2026-foe-forest-errors-makes-first/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kehan Jiang, Haonan Dong, Zhaolu Kang, et al.*2026 · 15 citations*
13. [**Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models arXiv:2505.03469**Bin Yu, Hang Yuan, Haotian Li, et al.*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/yu-2025-long-short-chain-thought-mixture/)
14. [**TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression arXiv:2506.02678**Zhong-Zhi Li, Xiao Liang, Zihao Tang, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-tl-dr-too-long-re/)
15. **[The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis](https://md.research.elamur.ai/free/en/papers/wei-2026-evolution-thought-tracking-llm-overthinking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zihao Wei, Liang Pang, Jiahao Liu, et al.*2026 · 6 citations*
16. **[DeepPrune: Parallel Scaling without Inter-trace Redundancy](https://md.research.elamur.ai/free/en/papers/tu-2026-deepprune-parallel-scaling-without-inter/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shangqing Tu, Yaxuan Li, Yushi Bai, et al.*2026 · 3 citations*
17. **[SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking](https://md.research.elamur.ai/free/en/papers/huang-2026-sat-balancing-reasoning-accuracy-efficiency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weiyang Huang, Xuefeng Bai, Kehai Chen, et al.*2026 · 3 citations*
18. **[Do LLMs Encode Functional Importance of Reasoning Tokens ?](https://md.research.elamur.ai/free/en/papers/singh-2026-do-llms-encode-functional-importance/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Janvijay Singh, Dilek Hakkani-Tür*2026 · 2 citations*
19. **[Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/liu-2026-think-how-think-mitigating-overthinking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yongjiang Liu, Haoxi Li, Xiaosong Ma, et al.*2026 · 2 citations*
20. **[Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/liu-2026-not-all-tokens-matter-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hanbing Liu, Lang Cao, Yuanyi Ren, et al.*2026 · 1 citation*
21. **[Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2026-render-thought-rendering-textual-chain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifan Wang, Shiyu Li, Peiming Li, et al.*2026 · 1 citation*
22. **[ThinkBrake: Efficient Reasoning via Log-Probability Margin Guided Decoding](https://md.research.elamur.ai/free/en/papers/song-2026-thinkbrake-efficient-reasoning-log-probability/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sangjun Song, Minjae Oh, Seungkyu Lee, et al.*2026 · 1 citation*
23. **[AdapThink: Adaptive Thinking Preferences for Reasoning Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-adapthink-adaptive-thinking-preferences-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenyue Xu, Xu Wan, Wei Wang, et al.*2026*
24. **[CLARO: Controlled Attribute-Driven Reasoning Optimization for Efficient Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/schlesinger-2026-claro-controlled-attribute-driven-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Oded Schlesinger, Young Kyung Kim, J. Matias Di Martino, et al.*2026*
25. **[Exploration-Exploitation Reshaping towards Efficient Reasoning for Large Language Models](https://md.research.elamur.ai/free/en/papers/shi-2026-exploration-exploitation-reshaping-efficient-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yufeng Shi, Weilin Luo, Yuxiang Zhang, et al.*2026*
26. **[Multilingual Chain-of-Thought Compression via Cross-Lingual Distillation](https://md.research.elamur.ai/free/en/papers/wan-2026-multilingual-chain-thought-compression-cross/) [Workshop on Multilinguality in the Era of Large Language Models](https://md.research.elamur.ai/free/en/venues/mellm/)**Jiarui Wan, Songming Zhang, Yufeng Chen*2026*
27. **[Shorten After You're Right: Lazy Length Penalties for Reasoning RL](https://md.research.elamur.ai/free/en/papers/yuan-2026-shorten-after-you-re-right/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Danlong Yuan, Tian Xie, Shaohan Huang, et al.*2026*
28. **[TrigReason: Trigger-Based Collaboration between Small and Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/zhao-2026-trigreason-trigger-based-collaboration-between/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yi Zhao, Yajuan Peng, Cam-Tu Nguyen, et al.*2026*

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

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