https://github.com/hemingkx/Awesome-Efficient-Reasoning
Paper list for Efficient Reasoning.
https://github.com/hemingkx/Awesome-Efficient-Reasoning
List: Awesome-Efficient-Reasoning
chain-of-thought efficient-reasoning
Last synced: about 2 months ago
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Paper list for Efficient Reasoning.
- Host: GitHub
- URL: https://github.com/hemingkx/Awesome-Efficient-Reasoning
- Owner: hemingkx
- License: apache-2.0
- Created: 2025-03-13T00:36:14.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-29T07:18:46.000Z (about 2 months ago)
- Last Synced: 2025-03-29T08:23:38.246Z (about 2 months ago)
- Topics: chain-of-thought, efficient-reasoning
- Homepage:
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- Stars: 331
- Watchers: 2
- Forks: 12
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - Awesome-Efficient-Reasoning - Paper list for Efficient Reasoning. (Other Lists / Julia Lists)
- Awesome_Efficient_LRM_Reasoning - hemingkx/Awesome-Efficient-Reasoning
- Awesome-Latent-CoT - Awesome-Efficient-Reasoning
README
This repository contains a regularly updated paper list for **Efficient Reasoning**.
[](https://awesome.re) [](./LICENSE)  
## Content
- [Keywords Convention](#keywords-convention)
- [Papers](#papers)
- [Survey](#survey)
- [Efficient Training](#efficient-training)
- [Latent Chain-of-Thought](#latent-chain-of-thought)
- [Chain-of-Thought Compression](#chain-of-thought-compression)
- [Balanced Chain-of-Thought](#balanced-chain-of-thought)
- [Reasoning Shortcuts](#reasoning-shortcuts)
- [Reasoning Step Decomposition](#reasoning-step-decomposition)
- [Small Reasoning Models & CoT Distillation](#small-reasoning-models--cot-distillation)
- [Optimal Test-Time Scaling](#optimal-test-time-scaling)
- [Efficient Sampling](#efficient-sampling)
- [Efficient Self-Consistency](#efficient-self-consistency)
- [Long-Context Reasoning Efficiency](#long-context-reasoning-efficiency)
- [Other Work](#other-work)
- [Benchmarks](#benchmarks)
- [Analysis](#analysis)
- [Blogs](#blog--project)
- [Talks](#talks)
- [Resources](#resources)
- [Contribution](#contribution)
- [Contributors](#contributors)
- [Contributing to this paper list](#contributing-to-this-paper-list)
## Keywords Convention
 Abbreviation
 Conference
 Main Features
## Papers
### Survey
- **Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models**
*Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen (Henry) Zhong, Hanjie Chen, Xia Hu*. [[pdf](https://arxiv.org/pdf/2503.16419)], [[paper list](https://github.com/Eclipsess/Awesome-Efficient-Reasoning-LLMs)], 2025.03. 
- **A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond**
*Xiaoye Qu, Yafu Li, Zhaochen Su, Weigao Sun, Jianhao Yan, Dongrui Liu, Ganqu Cui, Daizong Liu, Shuxian Liang, Junxian He, Peng Li, Wei Wei, Jing Shao, Chaochao Lu, Yue Zhang, Xian-Sheng Hua, Bowen Zhou, Yu Cheng*. [[pdf](https://arxiv.org/pdf/2503.21614)], [[paper list](https://github.com/XiaoYee/Awesome_Efficient_LRM_Reasoning)], 2025.03. 
- **Efficient Inference for Large Reasoning Models: A Survey**
*Yue Liu, Jiaying Wu, Yufei He, Hongcheng Gao, Hongyu Chen, Baolong Bi, Jiaheng Zhang, Zhiqi Huang, Bryan Hooi*. [[pdf](https://arxiv.org/pdf/2503.23077)], [[paper list](https://github.com/yueliu1999/Awesome-Efficient-Inference-for-LRMs)], 2025.03. 
- **Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models**
*Rui Wang, Hongru Wang, Boyang Xue, Jianhui Pang, Shudong Liu, Yi Chen, Jiahao Qiu, Derek Fai Wong, Heng Ji, Kam-Fai Wong*. [[pdf](https://arxiv.org/pdf/2503.24377)], [[paper list](https://github.com/DevoAllen/Awesome-Reasoning-Economy-Papers)], 2025.03. 
### Efficient Training
- **s1: Simple test-time scaling**
*Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, Tatsunori Hashimoto*. [[pdf](https://arxiv.org/pdf/2501.19393)], [[code](https://github.com/simplescaling/s1)], 2025.01.  
- **LIMO: Less is More for Reasoning**
*Yixin Ye, Zhen Huang, Yang Xiao, Ethan Chern, Shijie Xia, Pengfei Liu*. [[pdf](https://arxiv.org/pdf/2502.03387)], [[code](https://github.com/GAIR-NLP/LIMO)], 2025.02.  
- **Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond**
*Liang Wen, Yunke Cai, Fenrui Xiao, Xin He, Qi An, Zhenyu Duan, Yimin Du, Junchen Liu, Lifu Tang, Xiaowei Lv, Haosheng Zou, Yongchao Deng, Shousheng Jia, Xiangzheng Zhang*. [[pdf](https://arxiv.org/pdf/2503.10460)], [[code](https://github.com/Qihoo360/Light-R1)], 2025.03.  
- **DAPO: An Open-Source LLM Reinforcement Learning System at Scale**
*Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, Haibin Lin, Zhiqi Lin, Bole Ma, Guangming Sheng, Yuxuan Tong, Chi Zhang, Mofan Zhang, Wang Zhang, Hang Zhu, Jinhua Zhu, Jiaze Chen, Jiangjie Chen, Chengyi Wang, Hongli Yu, Weinan Dai, Yuxuan Song, Xiangpeng Wei, Hao Zhou, Jingjing Liu, Wei-Ying Ma, Ya-Qin Zhang, Lin Yan, Mu Qiao, Yonghui Wu, Mingxuan Wang*. [[pdf](https://arxiv.org/pdf/2503.14476)], [[code](https://github.com/BytedTsinghua-SIA/DAPO)], [[homepage](https://dapo-sia.github.io/)], 2025.03.  
- **FastCuRL: Curriculum Reinforcement Learning with Progressive Context Extension for Efficient Training R1-like Reasoning Models**
*Mingyang Song, Mao Zheng, Zheng Li, Wenjie Yang, Xuan Luo, Yue Pan, Feng Zhang*. [[pdf](https://arxiv.org/pdf/2503.17287)], [[code](https://github.com/nick7nlp/FastCuRL)], 2025.03.  
- **Understanding R1-Zero-Like Training: A Critical Perspective**
*Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, Min Lin*. [[pdf](https://arxiv.org/pdf/2503.20783)], [[code](https://github.com/sail-sg/understand-r1-zero)], 2025.03.  
- **Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training**
*Brian R. Bartoldson, Siddarth Venkatraman, James Diffenderfer, Moksh Jain, Tal Ben-Nun, Seanie Lee, Minsu Kim, Johan Obando-Ceron, Yoshua Bengio, Bhavya Kailkhura*. [[pdf](https://arxiv.org/pdf/2503.18929)], 2025.03.  
- **CPPO: Accelerating the Training of Group Relative Policy Optimization-Based Reasoning Models**
*Zhihang Lin, Mingbao Lin, Yuan Xie, Rongrong Ji*. [[pdf](https://arxiv.org/pdf/2503.22342)], [[code](https://github.com/lzhxmu/CPPO)], 2025.03.  
### Latent Chain-of-Thought
> For most recent latentCoT research, see [Awesome-Latent-CoT](https://github.com/EIT-NLP/Awesome-Latent-CoT).
- **Think before you speak: Training Language Models With Pause Tokens**
*Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan*. [[pdf](https://openreview.net/pdf?id=ph04CRkPdC)], 2023.10. 
- **Guiding Language Model Reasoning with Planning Tokens**
*Xinyi Wang, Lucas Caccia, Oleksiy Ostapenko, Xingdi Yuan, William Yang Wang, Alessandro Sordoni*. [[pdf](https://openreview.net/pdf?id=wi9IffRhVM)], 2023.10. 
- **Implicit Chain of Thought Reasoning via Knowledge Distillation**
*Yuntian Deng, Kiran Prasad, Roland Fernandez, Paul Smolensky, Vishrav Chaudhary, Stuart Shieber*. [[pdf](https://arxiv.org/pdf/2311.01460)], 2023.11. 
- **Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models**
*Jiacheng Ye, Shansan Gong, Liheng Chen, Lin Zheng, Jiahui Gao, Han Shi, Chuan Wu, Xin Jiang, Zhenguo Li, Wei Bi, Lingpeng Kong*. [[pdf](https://openreview.net/pdf?id=G0v0TxX01N)], 2024.02.  
- **Let's Think Dot by Dot: Hidden Computation in Transformer Language Models**
*Jacob Pfau, William Merrill, Samuel R. Bowman*. [[pdf](https://openreview.net/pdf?id=NikbrdtYvG)], 2024.04.  
- **From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step**
*Yuntian Deng, Yejin Choi, Stuart Shieber*. [[pdf](https://arxiv.org/pdf/2405.14838)], 2024.05.  
- **Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding**
*Tianqiao Liu, Zui Chen, Zitao Liu, Mi Tian, Weiqi Luo*. [[pdf](https://arxiv.org/pdf/2409.08561)], 2024.09. 
- **Do LLMs Really Think Step-by-step In Implicit Reasoning?**
*Yijiong Yu*. [[pdf](https://arxiv.org/pdf/2411.15862v3)], 2024.11.  
- **Disentangling Memory and Reasoning Ability in Large Language Models**
*Mingyu Jin, Weidi Luo, Sitao Cheng, Xinyi Wang, Wenyue Hua, Ruixiang Tang, William Yang Wang, Yongfeng Zhang*. [[pdf](https://arxiv.org/pdf/2411.13504)], [[code](https://github.com/MingyuJ666/Disentangling-Memory-and-Reasoning)], 2024.11. 
- **Training Large Language Models to Reason in a Continuous Latent Space**
*Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, Yuandong Tian*. [[pdf](https://openreview.net/pdf?id=G0v0TxX01N)], [[code](https://github.com/facebookresearch/coconut)], 2024.12.  
- **Compressed Chain of Thought: Efficient Reasoning Through Dense Representations**
*Jeffrey Cheng, Benjamin Van Durme*. [[pdf](https://arxiv.org/pdf/2412.13171)], 2024.12.  
- **Efficient Reasoning with Hidden Thinking**
*Xuan Shen, Yizhou Wang, Xiangxi Shi, Yanzhi Wang, Pu Zhao, Jiuxiang Gu*. [[pdf](https://arxiv.org/pdf/2501.19201)], 2025.01.   
- **Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking**
*Yilong Chen, Junyuan Shang, Zhenyu Zhang, Yanxi Xie, Jiawei Sheng, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang*. [[pdf](https://arxiv.org/pdf/2502.13842)], 2025.02.  
- **LightThinker: Thinking Step-by-Step Compression**
*Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang*. [[pdf](https://arxiv.org/pdf/2502.15589)], [[code](https://github.com/zjunlp/LightThinker)], 2025.02.  
- **Reasoning with Latent Thoughts: On the Power of Looped Transformers**
*Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li, Sanjiv Kumar, Sashank J. Reddi*. [[pdf](https://openreview.net/pdf?id=din0lGfZFd)], 2025.02. 
- **CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation**
*Zhenyi Shen, Hanqi Yan, Linhai Zhang, Zhanghao Hu, Yali Du, Yulan He*. [[pdf](https://arxiv.org/pdf/2502.21074)], 2025.02.  
- **Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach**
*Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Tom Goldstein*. [[pdf](https://arxiv.org/pdf/2502.05171)], [[code](https://github.com/seal-rg/recurrent-pretraining)], 2025.02. 
- **LLM Pretraining with Continuous Concepts**
*Jihoon Tack, Jack Lanchantin, Jane Yu, Andrew Cohen, Ilia Kulikov, Janice Lan, Shibo Hao, Yuandong Tian, Jason Weston, Xian Li*. [[pdf](https://arxiv.org/pdf/2502.08524)], [[code](https://github.com/facebookresearch/RAM/tree/main/projects/cocomix)], 2025.02.   
- **Scalable Language Models with Posterior Inference of Latent Thought Vectors**
*Deqian Kong, Minglu Zhao, Dehong Xu, Bo Pang, Shu Wang, Edouardo Honig, Zhangzhang Si, Chuan Li, Jianwen Xie, Sirui Xie, Ying Nian Wu*. [[pdf](https://arxiv.org/pdf/2502.01567)], 2025.02.  
- **Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning**
*Qifan Yu, Zhenyu He, Sijie Li, Xun Zhou, Jun Zhang, Jingjing Xu, Di He*. [[pdf](https://arxiv.org/pdf/2502.08482)], [[code](https://github.com/qifanyu/RELAY)], 2025.02.  
- **Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning**
*DiJia Su, Hanlin Zhu, Yingchen Xu, Jiantao Jiao, Yuandong Tian, Qinqing Zheng*. [[pdf](https://arxiv.org/pdf/2502.03275)], 2025.02. 
- **SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs**
*Yige Xu, Xu Guo, Zhiwei Zeng, Chunyan Miao*. [[pdf](https://arxiv.org/pdf/2502.12134)], 2025.02.  
- **Implicit Reasoning in Transformers is Reasoning through Shortcuts**
*Tianhe Lin, Jian Xie, Siyu Yuan, Deqing Yang*. [[pdf](https://arxiv.org/pdf/2503.07604)], 2025.03.  
- **Reasoning to Learn from Latent Thoughts**
*Yangjun Ruan, Neil Band, Chris J. Maddison, Tatsunori Hashimoto*. [[pdf](https://arxiv.org/pdf/2503.18866)], 2025.03. 
- **Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation**
*Jiakai Tang, Sunhao Dai, Teng Shi, Jun Xu, Xu Chen, Wen Chen, Wu Jian, Yuning Jiang*. [[pdf](https://arxiv.org/pdf/2503.22675)], 2025.03.   
### Chain-of-Thought Compression
- **Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models**
*Hanxu Hu, Hongyuan Lu, Huajian Zhang, Yun-Ze Song, Wai Lam, Yue Zhang*. [[pdf](https://arxiv.org/pdf/2305.10276)], [[code](https://github.com/hanxuhu/chain-of-symbol-planning)], 2023.05. 
- **The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models**
*Matthew Renze, Erhan Guven*. [[pdf](https://arxiv.org/pdf/2401.05618)], [[code](https://github.com/matthewrenze/jhu-concise-cot)], 2024.12.  
- **C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness**
*Yu Kang, Xianghui Sun, Liangyu Chen, Wei Zou*. [[pdf](https://arxiv.org/pdf/2412.11664)], 2024.12.  
- **Token-Budget-Aware LLM Reasoning**
*Tingxu Han, Zhenting Wang, Chunrong Fang, Shiyu Zhao, Shiqing Ma, Zhenyu Chen*. [[pdf](https://arxiv.org/pdf/2412.18547)], [[code](https://github.com/GeniusHTX/TALE)], 2024.12.   
- **CoT-Valve: Length-Compressible Chain-of-Thought Tuning**
*Xinyin Ma, Guangnian Wan, Runpeng Yu, Gongfan Fang, Xinchao Wang*. [[pdf](https://arxiv.org/pdf/2502.09601)], 2025.02.  
- **TokenSkip: Controllable Chain-of-Thought Compression in LLMs**
*Heming Xia, Yongqi Li, Chak Tou Leong, Wenjie Wang, Wenjie Li*. [[pdf](https://arxiv.org/pdf/2502.12067)], [[code](https://github.com/hemingkx/TokenSkip)], 2025.02.   
- **Self-Training Elicits Concise Reasoning in Large Language Models**
*Tergel Munkhbat, Namgyu Ho, Seo Hyun Kim, Yongjin Yang, Yujin Kim, Se-Young Yun*. [[pdf](https://arxiv.org/pdf/2502.20122)], [[code](https://github.com/TergelMunkhbat/concise-reasoning)], 2025.02. 
- **Chain of Draft: Thinking Faster by Writing Less**
*Silei Xu, Wenhao Xie, Lingxiao Zhao, Pengcheng He*. [[pdf](https://arxiv.org/pdf/2502.18600)], [[code](https://github.com/sileix/chain-of-draft)], 2025.02.   
- **How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach**
*Ayeong Lee, Ethan Che, Tianyi Peng*. [[pdf](https://arxiv.org/pdf/2503.01141)], [[code](https://github.com/Compressed-CoT/compressed-cot)], 2025.03.  
- **Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching**
*Simon A. Aytes, Jinheon Baek, Sung Ju Hwang*. [[pdf](https://arxiv.org/pdf/2503.05179)], [[code](https://github.com/SimonAytes/SoT)], 2025.03.   
- **Adaptive Group Policy Optimization: Towards Stable Training and Token-Efficient Reasoning**
*Chen Li, Nazhou Liu, Kai Yang*. [[pdf](https://arxiv.org/pdf/2503.15952)], 2025.03.   
- **Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging**
*Han Wu, Yuxuan Yao, Shuqi Liu, Zehua Liu, Xiaojin Fu, Xiongwei Han, Xing Li, Hui-Ling Zhen, Tao Zhong, Mingxuan Yuan*. [[pdf](https://arxiv.org/pdf/2503.20641)], [[code](https://github.com/hahahawu/Long-to-Short-via-Model-Merging)], 2025.03.  
### Balanced Chain-of-Thought
> Balanced CoT allocates more compute to hard questions, reduces compute for simpler ones.
- **Efficiently Serving LLM Reasoning Programs with Certaindex**
*Yichao Fu, Junda Chen, Siqi Zhu, Zheyu Fu, Zhongdongming Dai, Aurick Qiao, Hao Zhang*. [[pdf](https://arxiv.org/pdf/2412.20993)], 2024.12.  
- **O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning**
*Haotian Luo, Li Shen, Haiying He, Yibo Wang, Shiwei Liu, Wei Li, Naiqiang Tan, Xiaochun Cao, Dacheng Tao*. [[pdf](https://arxiv.org/pdf/2501.12570)], [[code](https://github.com/StarDewXXX/O1-Pruner)], 2025.01.  
- **Kimi k1.5: Scaling Reinforcement Learning with LLMs**
*Kimi Team*. [[pdf](https://arxiv.org/pdf/2501.12599)], 2025.01.  
- **Training Language Models to Reason Efficiently**
*Daman Arora, Andrea Zanette*. [[pdf](https://arxiv.org/pdf/2502.04463)], [[code](https://github.com/Zanette-Labs/efficient-reasoning)], [[homepage](https://zanette-labs.github.io/efficient-reasoning/)], 2025.02. 
- **Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models**
*Yuan Sui, Yufei He, Tri Cao, Simeng Han, Bryan Hooi*. [[pdf](https://arxiv.org/pdf/2502.19918)], 2025.02.  
- **DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models**
*Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang, Shiguo Lian*. [[pdf](https://arxiv.org/pdf/2503.04472)], 2025.03.  
### Reasoning Shortcuts
- **Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts**
*Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini*. [[pdf](https://arxiv.org/pdf/2305.19951)], 2023.05. 
- **Break the Chain: Large Language Models Can be Shortcut Reasoners**
*Mengru Ding, Hanmeng Liu, Zhizhang Fu, Jian Song, Wenbo Xie, Yue Zhang*. [[pdf](https://arxiv.org/pdf/2406.06580)], 2024.06.  
- **Can Language Models Learn to Skip Steps?**
*Tengxiao Liu, Qipeng Guo, Xiangkun Hu, Cheng Jiayang, Yue Zhang, Xipeng Qiu, Zheng Zhang*. [[pdf](https://arxiv.org/pdf/2411.01855)], [[code](https://github.com/tengxiaoliu/LM_skip)], 2024.11.  
- **TokenSkip: Controllable Chain-of-Thought Compression in LLMs**
*Heming Xia, Yongqi Li, Chak Tou Leong, Wenjie Wang, Wenjie Li*. [[pdf](https://arxiv.org/pdf/2502.12067)], [[code](https://github.com/hemingkx/TokenSkip)], 2025.02.   
- **Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models**
*Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Yue Xing, Jiliang Tang, Qi He*. [[pdf](https://arxiv.org/pdf/2502.13260)], 2025.02.  
### Reasoning Step Decomposition
- **Markov Chain of Thought for Efficient Mathematical Reasoning**
*Wen Yang, Minpeng Liao, Kai Fan*. [[pdf](https://arxiv.org/pdf/2410.17635)], [[code](https://github.com/james-yw/Markov-Chain-of-Thought)], 2024.10.   
- **Atom of Thoughts for Markov LLM Test-Time Scaling**
*Fengwei Teng, Zhaoyang Yu, Quan Shi, Jiayi Zhang, Chenglin Wu, Yuyu Luo*. [[pdf](https://arxiv.org/pdf/2502.12018)], [[code](https://github.com/qixucen/atom)], 2025.02.  
- **DISC: Dynamic Decomposition Improves LLM Inference Scaling**
*Jonathan Light, Wei Cheng, Wu Yue, Masafumi Oyamada, Mengdi Wang, Santiago Paternain, Haifeng Chen*. [[pdf](https://arxiv.org/pdf/2502.16706)], 2025.02.  
- **Can Atomic Step Decomposition Enhance the Self-structured Reasoning of Multimodal Large Models?**
*Kun Xiang, Zhili Liu, Zihao Jiang, Yunshuang Nie, Kaixin Cai, Yiyang Yin, Runhui Huang, Haoxiang Fan, Hanhui Li, Weiran Huang, Yihan Zeng, Yu-Jie Yuan, Jianhua Han, Lanqing Hong, Hang Xu, Xiaodan Liang*. [[pdf](https://arxiv.org/pdf/2503.06252)], [[code](https://github.com/Quinn777/AtomThink)], 2025.03.   
- **From Chaos to Order: The Atomic Reasoner Framework for Fine-grained Reasoning in Large Language Models**
*Jinyi Liu, Yan Zheng, Rong Cheng, Qiyu Wu, Wei Guo, Fei Ni, Hebin Liang, Yifu Yuan, Hangyu Mao, Fuzheng Zhang, Jianye Hao*. [[pdf](https://arxiv.org/pdf/2503.15944)], 2025.03.  
### Small Reasoning Models & CoT Distillation
- **Teaching Small Language Models to Reason**
*Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, Aliaksei Severyn*. [[pdf](https://aclanthology.org/2023.acl-short.151.pdf)], 2022.12. 
- **Mixed Distillation Helps Smaller Language Model Better Reasoning**
*Chenglin Li, Qianglong Chen, Liangyue Li, Caiyu Wang, Yicheng Li, Zulong Chen, Yin Zhang*. [[pdf](https://arxiv.org/pdf/2312.10730)], 2023.12. 
- **Small Language Models Need Strong Verifiers to Self-Correct Reasoning**
*Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim, Moontae Lee, Honglak Lee, Lu Wang*. [[pdf](https://arxiv.org/pdf/2404.17140)], [[code](https://github.com/yunx-z/SCORE)], 2024.04. 
- **Distilling Reasoning Ability from Large Language Models with Adaptive Thinking**
*Xiaoshu Chen, Sihang Zhou, Ke Liang, Xinwang Liu*. [[pdf](https://arxiv.org/pdf/2404.09170)], 2024.04. 
- **Teaching Small Language Models Reasoning through Counterfactual Distillation**
*Tao Feng, Yicheng Li, Li Chenglin, Hao Chen, Fei Yu, Yin Zhang*. [[pdf](https://aclanthology.org/2024.emnlp-main.333.pdf)], 2024.11. 
- **Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation**
*Xunyu Zhu, Jian Li, Can Ma, Weiping Wang*. [[pdf](https://arxiv.org/pdf/2411.14698)], 2024.11.  
- **Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners**
*Daniele Paliotta, Junxiong Wang, Matteo Pagliardini, Kevin Y. Li, Aviv Bick, J. Zico Kolter, Albert Gu, François Fleuret, Tri Dao*. [[pdf](https://arxiv.org/pdf/2502.20339)], 2025.02. 
- **Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning**
*Xinghao Chen, Zhijing Sun, Wenjin Guo, Miaoran Zhang, Yanjun Chen, Yirong Sun, Hui Su, Yijie Pan, Dietrich Klakow, Wenjie Li, Xiaoyu Shen*. [[pdf](https://arxiv.org/pdf/2502.18001)], [[code](https://github.com/EIT-NLP/Distilling-CoT-Reasoning)], 2025.02. 
- **Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling**
*Runze Liu, Junqi Gao, Jian Zhao, Kaiyan Zhang, Xiu Li, Biqing Qi, Wanli Ouyang, Bowen Zhou*. [[pdf](https://arxiv.org/pdf/2502.06703)], [[code](https://github.com/RyanLiu112/compute-optimal-tts)], [[homepage](https://ryanliu112.github.io/compute-optimal-tts/)], 2025.02.  
- **Small Models Struggle to Learn from Strong Reasoners**
*Yuetai Li, Xiang Yue, Zhangchen Xu, Fengqing Jiang, Luyao Niu, Bill Yuchen Lin, Bhaskar Ramasubramanian, Radha Poovendran*. [[pdf](https://arxiv.org/pdf/2502.12143)], [[code](https://github.com/Small-Model-Gap/Small-Model-Learnability-Gap)], [[homepage](https://small-model-gap.github.io/)], 2025.02. 
- **Towards Reasoning Ability of Small Language Models**
*Gaurav Srivastava, Shuxiang Cao, Xuan Wang*. [[pdf](https://arxiv.org/pdf/2502.11569)], 2025.02. 
- **Deconstructing Long Chain-of-Thought: A Structured Reasoning Optimization Framework for Long CoT Distillation**
*Yijia Luo, Yulin Song, Xingyao Zhang, Jiaheng Liu, Weixun Wang, GengRu Chen, Wenbo Su, Bo Zheng*. [[pdf](https://arxiv.org/pdf/2503.16385)], [[code](https://github.com/elena-luo/SODE)], 2025.03.  
- **SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild**
*Weihao Zeng, Yuzhen Huang, Qian Liu, Wei Liu, Keqing He, Zejun Ma, Junxian He*. [[pdf](https://arxiv.org/pdf/2503.18892)], [[code](https://github.com/hkust-nlp/simpleRL-reason)], 2025.03.   
- **Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't**
*Quy-Anh Dang, Chris Ngo*. [[pdf](https://arxiv.org/pdf/2503.16219)], [[code](https://github.com/knoveleng/open-rs)], 2025.03. 
- **TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers' Guidance**
*Jingxian Xu, Mengyu Zhou, Weichang Liu, Hanbing Liu, Shi Han, Dongmei Zhang*. [[pdf](https://arxiv.org/pdf/2503.24198)], 2025.03.  
### Optimal Test-Time Scaling
- **Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning**
*Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar*. [[pdf](https://openreview.net/pdf?id=4FWAwZtd2n)], 2024.08. 
- **Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models**
*Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, Yiming Yang*. [[pdf](https://arxiv.org/pdf/2408.00724)], [[code](https://github.com/thu-wyz/inference_scaling)], [[homepage](https://thu-wyz.github.io/inference-scaling/)], 2024.08. 
- **Scaling Test-Time Compute Without Verification or RL is Suboptimal**
*Amrith Setlur, Nived Rajaraman, Sergey Levine, Aviral Kumar*. [[pdf](https://arxiv.org/pdf/2502.12118)], 2025.02. 
- **Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?**
*Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, Yunhua Zhou, Xipeng Qiu*. [[pdf](https://arxiv.org/pdf/2502.12215)], [[code](https://github.com/ZhiYuanZeng/test-time-scaling-eval)], 2025.02. 
- **Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning**
*Wenkai Yang, Shuming Ma, Yankai Lin, Furu Wei*. [[pdf](https://arxiv.org/pdf/2502.18080)], 2025.02.  
- **Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning**
*Yuxiao Qu, Matthew Y. R. Yang, Amrith Setlur, Lewis Tunstall, Edward Emanuel Beeching, Ruslan Salakhutdinov, Aviral Kumar*. [[pdf](https://arxiv.org/pdf/2503.07572)], [[code](https://github.com/CMU-AIRe/MRT)], [[homepage](https://cohenqu.github.io/mrt.github.io/)], 2025.03.  
- **Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment**
*Audrey Huang, Adam Block, Qinghua Liu, Nan Jiang, Dylan J. Foster, Akshay Krishnamurthy*. [[pdf](https://arxiv.org/pdf/2503.21878)], 2025.03.  
- **What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models**
*Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, Lei Wang, Weixu Zhang, Zhihan Guo, Yufei Wang, Irwin King, Xue Liu, Chen Ma*. [[pdf](https://arxiv.org/pdf/2503.24235)], 2025.03.  
- **When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning**
*Nishad Singhi, Hritik Bansal, Arian Hosseini, Aditya Grover, Kai-Wei Chang, Marcus Rohrbach, Anna Rohrbach*. [[pdf](https://arxiv.org/pdf/2504.01005)], [[code](https://github.com/nishadsinghi/sc-genrm-scaling)], 2025.04. 
- **Z1: Efficient Test-time Scaling with Code**
*Zhaojian Yu, Yinghao Wu, Yilun Zhao, Arman Cohan, Xiao-Ping Zhang*. [[pdf](https://arxiv.org/pdf/2504.00810)], [[code](https://github.com/efficientscaling/Z1)], 2025.04.  
### Efficient Sampling
- **Fast Best-of-N Decoding via Speculative Rejection**
*Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter Bartlett, Andrea Zanette*. [[pdf](https://openreview.net/pdf?id=348hfcprUs)], [[code](https://github.com/Zanette-Labs/SpeculativeRejection)], 2024.10.   
- **Non-myopic Generation of Language Models for Reasoning and Planning**
*Chang Ma, Haiteng Zhao, Junlei Zhang, Junxian He, Lingpeng Kong*. [[pdf](https://openreview.net/pdf?id=OoNazl6T7D)], [[code](https://github.com/chang-github-00/LLM-Predictive-Decoding)], 2024.10.  
- **FastMCTS: A Simple Sampling Strategy for Data Synthesis**
*Peiji Li, Kai Lv, Yunfan Shao, Yichuan Ma, Linyang Li, Xiaoqing Zheng, Xipeng Qiu, Qipeng Guo*. [[pdf](https://www.arxiv.org/pdf/2502.11476)], 2025.02.  
- **Dynamic Parallel Tree Search for Efficient LLM Reasoning**
*Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, Xianglong Liu, Dacheng Tao*. [[pdf](https://arxiv.org/pdf/2502.16235)], 2025.02.  
- **Don't Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls**
*Ante Wang, Linfeng Song, Ye Tian, Dian Yu, Haitao Mi, Xiangyu Duan, Zhaopeng Tu, Jinsong Su, Dong Yu*. [[pdf](https://arxiv.org/pdf/2502.11183)], 2025.02.  
- **Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decoding**
*Yiming Wang, Pei Zhang, Siyuan Huang, Baosong Yang, Zhuosheng Zhang, Fei Huang, Rui Wang*. [[pdf](https://arxiv.org/pdf/2503.01422)], 2025.03.   
- **Language Models can Self-Improve at State-Value Estimation for Better Search**
*Ethan Mendes, Alan Ritter*. [[pdf](https://arxiv.org/pdf/2503.02878)], 2025.03.  
- **ϕ-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation**
*Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Jun Liu, Qika Lin, Zhiyong Wu*. [[pdf](https://arxiv.org/pdf/2503.13288)], [[code](https://github.com/xufangzhi/phi-Decoding)], 2025.03.  
### Efficient Self-Consistency
- **Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning**
*Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Xinglin Wang, Bin Sun, Heda Wang, Kan Li*. [[pdf](https://openreview.net/pdf?id=ndR8Ytrzhh)], [[code](https://github.com/Yiwei98/ESC)], 2024.01.  
- **Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning**
*Xinglin Wang, Shaoxiong Feng, Yiwei Li, Peiwen Yuan, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li*. [[pdf](https://arxiv.org/pdf/2408.13457)], [[code](https://github.com/WangXinglin/DSC)], 2024.08.  
- **Path-Consistency: Prefix Enhancement for Efficient Inference in LLM**
*Jiace Zhu, Yingtao Shen, Jie Zhao, An Zou*. [[pdf](https://arxiv.org/pdf/2409.01281)], 2024.08.  
- **Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling**
*Guangya Wan, Yuqi Wu, Jie Chen, Sheng Li*. [[pdf](https://arxiv.org/pdf/2408.17017)], [[code](https://github.com/wan19990901/RASC/tree/Submission_Code)], 2024.08.  
- **Efficient Test-Time Scaling via Self-Calibration**
*Chengsong Huang, Langlin Huang, Jixuan Leng, Jiacheng Liu, Jiaxin Huang*. [[pdf](https://arxiv.org/pdf/2503.00031)], [[code](https://github.com/Chengsong-Huang/Self-Calibration)], 2025.02.  
- **Confidence Improves Self-Consistency in LLMs**
*Amir Taubenfeld, Tom Sheffer, Eran Ofek, Amir Feder, Ariel Goldstein, Zorik Gekhman, Gal Yona*. [[pdf](https://arxiv.org/pdf/2502.06233)], 2025.02.  
- **Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning**
*Zhi Zhou, Tan Yuhao, Zenan Li, Yuan Yao, Lan-Zhe Guo, Xiaoxing Ma, Yu-Feng Li*. [[pdf](https://arxiv.org/pdf/2502.00511)], 2025.02.  
### Long-Context Reasoning Efficiency
- **OmniKV: Dynamic Context Selection for Efficient Long-Context LLMs**
*Jitai Hao, Yuke Zhu, Tian Wang, Jun Yu, Xin Xin, Bo Zheng, Zhaochun Ren, Sheng Guo*. [[pdf](https://openreview.net/pdf?id=ulCAPXYXfa)], 2024.10.  
- **InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models**
*Yuchen Yan, Yongliang Shen, Yang Liu, Jin Jiang, Mengdi Zhang, Jian Shao, Yueting Zhuang*. [[pdf](https://arxiv.org/pdf/2503.06692)], 2025.03.   
### Other Work
- **Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding**
*Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, Yu Wang*. [[pdf](https://openreview.net/pdf?id=mqVgBbNCm9)], [[code](https://github.com/imagination-research/sot)], [[homepage](https://sites.google.com/view/sot-llm)], 2023.06.   
- **Adaptive Skeleton Graph Decoding**
*Shuowei Jin, Yongji Wu, Haizhong Zheng, Qingzhao Zhang, Matthew Lentz, Z. Morley Mao, Atul Prakash, Feng Qian, Danyang Zhuo*. [[pdf](https://arxiv.org/pdf/2402.12280)], 2024.02.  
- **Reward-Guided Speculative Decoding for Efficient LLM Reasoning**
*Baohao Liao, Yuhui Xu, Hanze Dong, Junnan Li, Christof Monz, Silvio Savarese, Doyen Sahoo, Caiming Xiong*. [[pdf](https://arxiv.org/pdf/2501.19324)], [[code](https://github.com/BaohaoLiao/RSD)], 2025.01.   
- **L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning**
*Pranjal Aggarwal, Sean Welleck*. [[pdf](https://www.arxiv.org/pdf/2503.04697)], [[code](https://github.com/cmu-l3/l1)], [[homepage](https://cmu-l3.github.io/l1/)], 2025.03.   
- **PENCIL: Long Thoughts with Short Memory**
*PENCIL: Long Thoughts with Short Memory*. [[pdf](https://arxiv.org/pdf/2503.14337)], 2025.03.   
- **Accelerate Parallelizable Reasoning via Parallel Decoding within One Sequence**
*Yijiong Yu*. [[pdf](https://arxiv.org/pdf/2503.20533)], [[code](https://github.com/yuyijiong/parallel-decoding-in-one-sequence)], 2024.03.  
### Benchmarks
- **DNA Bench: When Silence is Smarter -- Benchmarking Over-Reasoning in Reasoning LLMs**
*Masoud Hashemi, Oluwanifemi Bamgbose, Sathwik Tejaswi Madhusudhan, Jishnu Sethumadhavan Nair, Aman Tiwari, Vikas Yadav*. [[pdf](https://arxiv.org/pdf/2503.15793)], 2024.12.   
### Analysis
- **The Impact of Reasoning Step Length on Large Language Models**
*Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du*. [[pdf](https://aclanthology.org/2024.findings-acl.108.pdf)], [[code](https://github.com/MingyuJ666/The-Impact-of-Reasoning-Step-Length-on-Large-Language-Models)], 2024.01.  
- **Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought**
*Qiguang Chen, Libo Qin, Jiaqi Wang, Jinxuan Zhou, Wanxiang Che*. [[pdf](https://arxiv.org/pdf/2410.05695)], [[code](https://github.com/LightChen233/reasoning-boundary)], 2024.10.  
- **Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs**
*Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, Dong Yu*. [[pdf](https://arxiv.org/pdf/2412.21187)], 2024.12.  
- **When More is Less: Understanding Chain-of-Thought Length in LLMs**
*Yuyang Wu, Yifei Wang, Tianqi Du, Stefanie Jegelka, Yisen Wang*. [[pdf](https://arxiv.org/pdf/2502.07266)], 2025.02.  
- **The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks**
*Alejandro Cuadron, Dacheng Li, Wenjie Ma, Xingyao Wang, Yichuan Wang, Siyuan Zhuang, Shu Liu, Luis Gaspar Schroeder, Tian Xia, Huanzhi Mao, Nicholas Thumiger, Aditya Desai, Ion Stoica, Ana Klimovic, Graham Neubig, Joseph E. Gonzalez*. [[pdf](https://arxiv.org/pdf/2502.08235)], [[code](https://github.com/AlexCuadron/ThinkingAgent)], 2025.02.  
- **Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs**
*Yue Wang, Qiuzhi Liu, Jiahao Xu, Tian Liang, Xingyu Chen, Zhiwei He, Linfeng Song, Dian Yu, Juntao Li, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, Dong Yu*. [[pdf](https://arxiv.org/pdf/2501.18585)], 2025.02.  
- **The Relationship Between Reasoning and Performance in Large Language Models -- o3 (mini) Thinks Harder, Not Longer**
*Marthe Ballon, Andres Algaba, Vincent Ginis*. [[pdf](https://arxiv.org/pdf/2502.15631)], 2025.02. 
- **Long Is More Important Than Difficult for Training Reasoning Models**
*Si Shen, Fei Huang, Zhixiao Zhao, Chang Liu, Tiansheng Zheng, Danhao Zhu*. [[pdf](https://arxiv.org/pdf/2503.18069)], [[code](https://huggingface.co/ZTss/LONG1k-32B)], 2025.03. 
- **Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking**
*Yuyao Ge, Shenghua Liu, Yiwei Wang, Lingrui Mei, Lizhe Chen, Baolong Bi, Xueqi Cheng*. [[pdf](https://arxiv.org/pdf/2503.19602)], 2025.03.  
## Blog & Project
**Optimizing LLM Test-Time Compute Involves Solving a Meta-RL Problem.** CMU, University of Toronto. [[blog](https://blog.ml.cmu.edu/2025/01/08/optimizing-llm-test-time-compute-involves-solving-a-meta-rl-problem/)], 2025.01.
**Understanding R1-Zero-Like Training: A Critical Perspective.** Sea AI Lab. [[paper](https://github.com/sail-sg/understand-r1-zero/blob/main/understand-r1-zero.pdf)], [[code](https://github.com/sail-sg/understand-r1-zero)], 2025.03.
## Talks
**The Key Ingredients for Scaling Test-Time Compute.** [Aviral Kumar](https://aviralkumar2907.github.io/). Carnegie Mellon University. [[homepage](https://www.cs.cmu.edu/calendar/181689023)], [[video](https://www.bilibili.com/video/BV1suAteHEXS)], 2025.03.
## Resources
**Reading lists related to Efficient Reasoning**
- [Eclipsess/Awesome-Efficient-Reasoning-LLMs](https://github.com/Eclipsess/Awesome-Efficient-Reasoning-LLMs)
- [XiaoYee/Awesome_Efficient_LRM_Reasoning](https://github.com/XiaoYee/Awesome_Efficient_LRM_Reasoning)
- [Blueyee/Efficient-CoT-LRMs](https://github.com/Blueyee/Efficient-CoT-LRMs)
- [yueliu1999/Awesome-Efficient-Inference-for-LRMs](https://github.com/yueliu1999/Awesome-Efficient-Inference-for-LRMs)
- [DevoAllen/Awesome-Reasoning-Economy-Papers](https://github.com/DevoAllen/Awesome-Reasoning-Economy-Papers)
- [Hongcheng-Gao/Awesome-Long2short-on-LRMs](https://github.com/Hongcheng-Gao/Awesome-Long2short-on-LRMs)
- [EIT-NLP/Awesome-Latent-CoT](https://github.com/EIT-NLP/Awesome-Latent-CoT)
- [yzhangchuck/awesome-llm-reasoning-long2short-papers](https://github.com/yzhangchuck/awesome-llm-reasoning-long2short-papers)
## Contributors
## Contributing to this paper list
- There are cases where we miss important works in this field, please feel free to contribute and promote your awesome work or other related works here! Thanks for the efforts in advance.