Awesome LLM Strawberry (OpenAI o1)

This is a collection of research papers & blogs for OpenAI Strawberry(o1) and Reasoning.

And the repository will be continuously updated to track the frontier of LLM Reasoning.

OpenAI Docs

• https://platform.openai.com/docs/guides/reasoning
• <img src="https://github.com/user-attachments/assets/b165cb20-9202-4951-8783-6b2f7e0d6071" width="600px">

News

• [OpenAI] Introducing deep research
• [OpenAI] o3 preview & o3 mini
• [OpenAI] Introducing ChatGPT Pro
• [Google DeepMind] Gemini 2.0 Flash Thinking
• [Ilya Sutskever] AI with reasoning power will be less predictable
• [SemianAlysis] Scaling Laws – O1 Pro Architecture, Reasoning Training Infrastructure, Orion and Claude 3.5 Opus “Failures”
• [DeepSeek] DeepSeek-R1-Lite-Preview is now live: unleashing supercharged reasoning power!
• [Moonshoot] 数学对标o1系列，搜索再次进化，Kimi 新推理模型与你一起拓展智能边界
• [Moonshoot] Kimi 发布视觉思考模型 k1，多项理科测试行业领先
• [InternLM] 强推理模型书生InternThinker开放体验：自主生成高智力密度数据、具备元动作思考能力
• [新智元] 万字独家爆光，首揭o1 pro架构！惊人反转，Claude 3.5 Opus没失败？

Blogs

• [OpenAI] Learning to Reason with LLMs
• [OpenAI] OpenAI o1-mini Advancing cost-efficient reasoning
• [OpenAI] Finding GPT-4’s mistakes with GPT-4
• [ARC-AGI] OpenAI o3 Breakthrough High Score on ARC-AGI-Pub
• [Anthropic] Building effective agents
• [hijkzzz] Stabilizing MoE RL Without Router Replay: The Online IcePop Solution
• [hijkzzz] REINFORCE++-baseline is all you need in RLVR
• [hijkzzz] Exploring OpenAI O1 Model Replication
• [Nathan Lambert] OpenAI’s Strawberry, LM self-talk, inference scaling laws, and spending more on inference
• [Nathan Lambert] Reverse engineering OpenAI’s o1
• [Andreas Stuhlmüller, jungofthewon] Supervise Process, not Outcomes
• [Nouha Dziri] Have o1 Models Cracked Human Reasoning?
• [Rishabh Agarwal] Improving LLM Reasoning using Self-generated data: RL and Verifiers
• [Wei Shen] Generalization Progress in RLHF: Insights into the Impact of Reward Models and PPO
• [Dominater069] Codeforces - Analyzing how good O1-Mini actually is
• [Tibor Blaho] Summary of what we have learned during AMA hour with the OpenAI o1 team

Talks

• [Noam Brown] Parables on the Power of Planning in AI: From Poker to Diplomacy
• [Noam Brown] OpenAI o1 and Teaching LLMs to Reason Better
• [Hyung Won Chung] Don't teach. Incentivize.

Courses

• [DeepLearning.AI] Reasoning with o1

Twitter

<details> <summary>OpenAI Developers</summary>

• All the questions addressed by the API team during the December 17, 2024 AMA
• We’re hosting an AMA for developers from 10–11 AM PT today.
• Today we previewed Reinforcement Fine-Tuning
• <img src="https://github.com/user-attachments/assets/8d33afdb-39ec-4c76-ab27-d132ea913c3e" width="360px">

</details> <details> <summary>Noam Brown</summary>

• <img src="https://github.com/user-attachments/assets/e33e5dd6-0c03-43b0-98a5-620176c337c2" width="360px">
• <img src="https://github.com/user-attachments/assets/4670514c-e6fa-474f-abea-c3f6ad01e41a" width="360px">
• <img src="https://github.com/user-attachments/assets/b390ccea-9773-4a96-ba02-40d917473402" width="360px">
• <img src="https://github.com/user-attachments/assets/aa0678fa-28eb-4b2a-a6ff-c0d123568f22" width="360px">

</details> <details> <summary>Jason Wei</summary>

• <img src="https://github.com/user-attachments/assets/fcf1ffa7-9e92-4cb6-afd4-ba6c14f3ae32" width="360px">
• <img src="https://github.com/user-attachments/assets/0f84796d-ae11-4c50-a681-e1b33aa152d9" width="360px">
• <img src="https://github.com/user-attachments/assets/fbbf78e4-d34c-4b7b-8163-f8c7288f56a6" width="360px">

</details> <details> <summary>Others</summary>

• <img src="https://github.com/user-attachments/assets/9c577f52-1976-4687-a67b-fd52a104a8cc" width="360px">
• <img src="https://github.com/user-attachments/assets/d3fd109b-0c97-4a94-931e-919b3b2f75f4" width="360px">
• <img src="https://github.com/user-attachments/assets/88896f70-017d-4520-ac56-370a023cfe45" width="360px">

</details>

Open-source

Models

• [Alibaba Qwen Team] Qwen3
• [Alibaba Qwen Team] QwQ
• [Alibaba Qwen Team] QvQ
• [DeepSeek] DeepSeek R1
• [NVIDIA] Nemotron-Research-Reasoning-Qwen-1.5B
• [Skywork] Skywork R1V2
• [rLLM] DeepScaler
• [NovaSky] Sky-T1
• [GAIR-NLP] O1 Replication Journey: A Strategic Progress Report
• [OpenO1 Team] Open-Source O1
• [Tencent] DRT-o1
• [Alibaba] Marco-o1
• [CUHK-SZ] HuatuoGPT-o1

Codebase

• [OpenRLHF Team] OpenRLHF
• [OpenRLHF Team] REINFORCE++ | REINFORCE++-baseline | Code
• [NovaSky-AI] SkyRL
• [RUCAIBox] STILL: Slow Thinking with LLMs
• [HKUST] Simple Reinforcement Learning for Reasoning
  • This is a replicate of DeepSeek-R1-Zero and DeepSeek-R1 training on small models with limited data
• [Ubiquant] Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning
• [StepFun] Open-Reasoner-Zero
• [TideDra] LMM-R1
• [ModalMinds] MM-EUREKA: Exploring Visual Aha Moment with Rule-based Large-scale Reinforcement Learning
• [R1-V Team] R1-V
• [LLaMA-Factory Team] EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework
• [Alibaba] ROLL | Paper
• [Sea AI Lab] Dr. GRPO
• [Berkeley AI Research] TinyZero
• [Maitrix.org] LLM Reasoners

Papers

format:
- [title](paper link) [links]
  - author1, author2, and author3...
  - publisher
  - code
  - experimental environments and datasets

Technical Report on o1 Models

• INTELLECT-3: Technical Report
  • Prime Intellect Team
• DeepSeek V3.2
  • DeepSeek AI
• MiMo-V2 Flash
  • Xiao Mi
• Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
  • NVIDIA
• Qwen3 Technical Report
  • Qwen Team
• Scaling Agents via Continual Pre-training
  • Qwen Team
• WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents
  • Qwen Team
• Towards General Agentic Intelligence via Environment Scaling
  • Qwen Team
• Magistral
  • Mistral AI
• ERINE-4.5
  • Baidu
• LongCat Flash
  • Meituan
• GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
  • Zhipu AI
• Seed Thinking v1.5
  • Bytedance Seed
• DeepSeek-V3 Technical Report
  • DeepSeek
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
  • DeepSeek AI
• DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition
  • DeepSeek AI
• MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
  • MiniMax
• Kimi k2: Open agentic intelligence
  • MoonShot
• Kimi k1.5: Scaling Reinforcement Learning with LLMs
  • MoonShot
• KIMI-VL TECHNICAL REPORT
  • MoonShot
• Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning
  • MoonShot & Numina
• Llama-Nemotron: Efficient Reasoning Models
  • NVIDIA
• Skywork Open Reasoner 1 Technical Report
  • Skywork

2025

• Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models
  • NVIDIA
• QwenLong-L1.5: Post-Training Recipe for Long-Context Reasoning and Memory
  • Qwen Team
• DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning
  • DeepSeek-AI
• Stabilizing Reinforcement Learning with LLMs: Formulation and Practices
  • Qwen Team
• The Art of Scaling Reinforcement Learning Compute for LLMs
  • Devvrit Khatri, Lovish Madaan, Rishabh Tiwari, Rachit Bansal, Sai Surya Duvvuri, Manzil Zaheer, Inderjit S. Dhillon, David Brandfonbrener, Rishabh Agarwal
• BroRL: Scaling Reinforcement Learning via Broadened Exploration
  • Jian Hu, Mingjie Liu, Ximing Lu, Fang Wu, Zaid Harchaoui, Shizhe Diao, Yejin Choi, Pavlo Molchanov, Jun Yang, Jan Kautz, Yi Dong
• Why Language Models Hallucinate
  • OpenAI
• rStar2-Agent: Agentic Reasoning Technical Report
  • Ning Shang, Yifei Liu, Yi Zhu, Li Lyna Zhang, Weijiang Xu, Xinyu Guan, Buze Zhang, Bingcheng Dong, Xudong Zhou, Bowen Zhang, Ying Xin, Ziming Miao, Scarlett Li, Fan Yang, Mao Yang
• RL Is Neither a Panacea Nor a Mirage: Understanding Supervised vs. Reinforcement Learning Fine-Tuning for LLMs
  • Hangzhan Jin, Sicheng, Sifan Wu, Mohammad Hamdaqa
• DuPO: Enabling Reliable LLM Self-Verification via Dual Preference Optimization
  • Shuaijie She, Yu Bao, Yu Lu, Lu Xu, Tao Li, Wenhao Zhu, Shujian Huang, Shanbo Cheng, Lu Lu, Yuxuan Wang
• Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning
  • Vaishnavi Shrivastava, Ahmed Awadallah, Vidhisha Balachandran, Shivam Garg, Harkirat Behl, Dimitris Papailiopoulos
• ProRL V2 - Prolonged Training Validates RL Scaling Laws
  • Jian Hu, Mingjie Liu, Shizhe Diao, Ximing Lu, Xin Dong, Pavlo Molchanov, Yejin Choi, Jan Kautz, Yi Dong
• Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
  • Zihe Liu, Jiashun Liu, Yancheng He, Weixun Wang, Jiaheng Liu, Ling Pan, Xinyu Hu, Shaopan Xiong, Ju Huang, Jian Hu, Shengyi Huang, Siran Yang, Jiamang Wang, Wenbo Su, Bo Zheng
• Group Sequence Policy Optimization
  • Chujie Zheng, Shixuan Liu, Mingze Li, Xiong-Hui Chen, Bowen Yu, Chang Gao, Kai Dang, Yuqiong Liu, Rui Men, An Yang, Jingren Zhou, Junyang Lin
• Your Efficient RL Framework Secretly Brings You Off-Policy RL Training
  • Feng Yao, Liyuan Liu, Dinghuai Zhang, Chengyu Dong, Jingbo Shang, Jianfeng Gao
• Gemini 2.5 Pro Capable of Winning Gold at IMO 2025
  • Yichen Huang, Lin F. Yang
• DeepSWE: Training a Fully Open-sourced, State-of-the-Art Coding Agent by Scaling RL
  • together.ai
• OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling
  • Zengzhi Wang, Fan Zhou, Xuefeng Li, Pengfei Liu
• ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
  • Mingjie Liu, Shizhe Diao, Ximing Lu, Jian Hu, Xin Dong, Yejin Choi, Jan Kautz, Yi Dong
• REINFORCE++: An Efficient RLHF Algorithm with Robustness to Both Prompt and Reward Models
  • Jian Hu, Jason Klein Liu, Wei Shen
  • Code: REINFORCE++-baseline
• Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
  • Qwen Team
• The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning
  • Xinyu Zhu, Mengzhou Xia, Zhepei Wei, Wei-Lin Chen, Danqi Chen, Yu Meng
• Thinking with Generated Images
  • Ethan Chern, Zhulin Hu, Steffi Chern, Siqi Kou, Jiadi Su, Yan Ma, Zhijie Deng, Pengfei Liu
• Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning
  • Alex Su, Haozhe Wang, Weiming Ren, Fangzhen Lin, Wenhu Chen
• DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning
  • Ziwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, Chao Shen, Xing Yu
• Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging
  • Shiqi Chen, Jinghan Zhang, Tongyao Zhu, Wei Liu, Siyang Gao, Miao Xiong, Manling Li, Junxian He
• QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
  • Fanqi Wan, Weizhou Shen, Shengyi Liao, Yingcheng Shi, Chenliang Li, Ziyi Yang, Ji Zhang, Fei Huang, Jingren Zhou, Ming Yan
• Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem Solving
  • Xinji Mai, Haotian Xu, Xing W, Weinong Wang, Yingying Zhang, Wenqiang Zhang
  • Code: https://github.com/yyht/openrlhf_async_pipline
• A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce
  • Wei Xiong, Jiarui Yao, Yuhui Xu, Bo Pang, Lei Wang, Doyen Sahoo, Junnan Li, Nan Jiang, Tong Zhang, Caiming Xiong, Hanze Dong
• Reinforcement Learning for Reasoning in Large Language Models with One Training Example
  • Yiping Wang, Qing Yang, Zhiyuan Zeng, Liliang Ren, Lucas Liu, Baolin Peng, Hao Cheng, Xuehai He, Kuan Wang, Jianfeng Gao, Weizhu Chen, Shuohang Wang, Simon Shaolei Du, Yelong Shen
• Process Reward Models That Think
  • Muhammad Khalifa, Rishabh Agarwal, Lajanugen Logeswaran, Jaekyeom Kim, Hao Peng, Moontae Lee, Honglak Lee, Lu Wang
• M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models
  • Junxiong Wang, Wen-Ding Li, Daniele Paliotta, Daniel Ritter, Alexander M. Rush, Tri Dao
• A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility
  • Andreas Hochlehnert, Hardik Bhatnagar, Vishaal Udandarao, Samuel Albanie, Ameya Prabhu, Matthias Bethge
• Concise Reasoning via Reinforcement Learning
  • Mehdi Fatemi, Banafsheh Rafiee, Mingjie Tang, Kartik Talamadupula
• VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks
  • YuYue, Yufeng Yuan, Qiying Yu, Xiaochen Zuo, Ruofei Zhu, Wenyuan Xu, Jiaze Chen, Chengyi Wang, TianTian Fan, Zhengyin Du, Xiangpeng Wei, Gaohong Liu, Juncai Liu, Lingjun Liu, Haibin Lin, Zhiqi Lin, Bole Ma, etc.
• Inference-Time Scaling for Generalist Reward Modeling
  • Zijun Liu, Peiyi Wang, Runxin Xu, Shirong Ma, Chong Ruan, Peng Li, Yang Liu, Yu Wu
• JudgeLRM: Large Reasoning Models as a Judge
  • Nuo Chen, Zhiyuan Hu, Qingyun Zou, Jiaying Wu, Qian Wang, Bryan Hooi, Bingsheng He
• 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, etc.
• 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
• 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
• R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
  • Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, Ji-Rong Wen
• Visual-RFT: Visual Reinforcement Fine-Tuning
  • Ziyu Liu, Zeyi Sun, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Haodong Duan, Dahua Lin, Jiaqi Wang
• Introducing Visual Perception Token into Multimodal Large Language Model
  • Runpeng Yu, Xinyin Ma, Xinchao Wang
• Scaling Test-Time Compute Without Verification or RL is Suboptimal
  • Amrith Setlur, Nived Rajaraman, Sergey Levine, Aviral Kumar
• LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!
  • Dacheng Li, Shiyi Cao, Tyler Griggs, Shu Liu, Xiangxi Mo, Shishir G. Patil, Matei Zaharia, Joseph E. Gonzalez, Ion Stoica
• Demystifying Long Chain-of-Thought Reasoning in LLMs
  • Edward Yeo, Yuxuan Tong, Morry Niu, Graham Neubig, Xiang Yue
• LIMR: Less is More for RL Scaling
  • Xuefeng Li, Haoyang Zou, Pengfei Liu
• LIMO: Less is More for Reasoning
  • Yixin Ye, Zhen Huang, Yang Xiao, Ethan Chern, Shijie Xia, Pengfei Liu
• 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
• SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
  • Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Dale Schuurmans, Quoc V. Le, Sergey Levine, Yi Ma
• Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling
  • Zhenyu Hou, Xin Lv, Rui Lu, Jiajie Zhang, Yujiang Li, Zijun Yao, Juanzi Li, Jie Tang, Yuxiao Dong
• Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
  • Maohao Shen, Guangtao Zeng, Zhenting Qi, Zhang-Wei Hong, Zhenfang Chen, Wei Lu, Gregory Wornell, Subhro Das, David Cox, Chuang Gan
• Distillation Quantification for Large Language Models
  • Sunbowen Lee, Junting Zhou, Chao Ao, etc.
• rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
  • Xinyu Guan, Li Lyna Zhang, Yifei Liu, Ning Shang, Youran Sun, Yi Zhu, Fan Yang, Mao Yang
• Evolving Deeper LLM Thinking
  • Kuang-Huei Lee, Ian Fischer, Yueh-Hua Wu, Dave Marwood, Shumeet Baluja, Dale Schuurmans, Xinyun Chen
• The Lessons of Developing Process Reward Models in Mathematical Reasoning
  • Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin
• Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought
  • Violet Xiang, Charlie Snell, Kanishk Gandhi, Alon Albalak, Anikait Singh, Chase Blagden, Duy Phung, Rafael Rafailov, Nathan Lile, Dakota Mahan, Louis Castricato, Jan-Philipp Franken, Nick Haber, Chelsea Finn
• PRMBENCH: A Fine-grained and Challenging Benchmark for Process-Level Reward Models
  • Mingyang Song, Zhaochen Su, Xiaoye Qu, Jiawei Zhou, Yu Cheng
• Virgo: A Preliminary Exploration on Reproducing o1-like MLLM
  • Yifan Du, Zikang Liu, Yifan Li, Wayne Xin Zhao, Yuqi Huo, Bingning Wang, Weipeng Chen, Zheng Liu, Zhongyuan Wang, Ji-Rong Wen
• Imagine while Reasoning in Space: Multimodal Visualization-of-Thought
  • Chengzu Li, Wenshan Wu, Huanyu Zhang, Yan Xia, Shaoguang Mao, Li Dong, Ivan Vulić, Furu Wei
• LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs
  • Omkar Thawakar, Dinura Dissanayake, Ketan More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, Hisham Cholakkal, Ivan Laptev, Mubarak Shah, Fahad Shahbaz Khan, Salman Khan
• 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

2024

• Deliberative alignment: reasoning enables safer language models
  • OpenAI
• MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
  • Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Lilian Weng, Aleksander Mądry
• From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond
  • Scott McKinney
• LLM Critics Help Catch LLM Bugs
  • Nat McAleese, Rai Michael Pokorny, Juan Felipe Ceron Uribe, Evgenia Nitishinskaya, Maja Trebacz, Jan Leike
• Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning
  • Yuexiang Zhai, Hao Bai, Zipeng Lin, Jiayi Pan, Shengbang Tong, Yifei Zhou, Alane Suhr, Saining Xie, Yann LeCun, Yi Ma, Sergey Levine
• ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Exploration
  • Haozhan Shen, Kangjia Zhao, Tiancheng Zhao, Ruochen Xu, Zilun Zhang, Mingwei Zhu, Jianwei Yin
• Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models
  • Wenbin Wang, Liang Ding, Minyan Zeng, Xiabin Zhou, Li Shen, Yong Luo, Dacheng Tao
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
  • Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar
• An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
  • Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, Yiming Yang
• Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling
  • Hritik Bansal, Arian Hosseini, Rishabh Agarwal, Vinh Q. Tran, Mehran Kazemi
• Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
  • Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, Azalia Mirhoseini
• Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems
  • Yingqian Min, Zhipeng Chen, Jinhao Jiang, Jie Chen, Jia Deng, Yiwen Hu, Yiru Tang, Jiapeng Wang, Xiaoxue Cheng, Huatong Song, Wayne Xin Zhao, Zheng Liu, Zhongyuan Wang, Ji-Rong Wen
• Training Language Models to Self-Correct via Reinforcement Learning
  • Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal Behbahani, Aleksandra Faust
• 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
• MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes
  • Asma Ben Abacha, Wen-wai Yim, Yujuan Fu, Zhaoyi Sun, Meliha Yetisgen, Fei Xia, Thomas Lin
• Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
  • An Yang, Beichen Zhang, Binyuan Hui, Bofei Gao, Bowen Yu, Chengpeng Li, Dayiheng Liu, Jianhong Tu, Jingren Zhou, Junyang Lin, Keming Lu, Mingfeng Xue, Runji Lin, Tianyu Liu, Xingzhang Ren, Zhenru Zhang
• Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
  • Zhenyu Hou, Pengfan Du, Yilin Niu, Zhengxiao Du, Aohan Zeng, Xiao Liu, Minlie Huang, Hongning Wang, Jie Tang, Yuxiao Dong
• Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering
  • Xinyan Guan, Yanjiang Liu, Xinyu Lu, Boxi Cao, Ben He, Xianpei Han, Le Sun, Jie Lou, Bowen Yu, Yaojie Lu, Hongyu Lin
• Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
  • Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, Bo Wang, Shimin Li, Yunhua Zhou, Qipeng Guo, Xuanjing Huang, Xipeng Qiu
• Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
  • Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, Noah D. Goodman
  • https://github.com/ezelikman/quiet-star
• Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision
  • Zhiheng Xi, Dingwen Yang, Jixuan Huang, Jiafu Tang, Guanyu Li, Yiwen Ding, Wei He, Boyang Hong, Shihan Do, Wenyu Zhan, Xiao Wang, Rui Zheng, Tao Ji, Xiaowei Shi, Yitao Zhai, Rongxiang Weng, Jingang Wang, Xunliang Cai, Tao Gui, Zuxuan Wu, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Yu-Gang Jiang
  • https://mathcritique.github.io/
• On Designing Effective RL Reward at Training Time for LLM Reasoning
  • Jiaxuan Gao, Shusheng Xu, Wenjie Ye, Weilin Liu, Chuyi He, Wei Fu, Zhiyu Mei, Guangju Wang, Yi Wu
• Generative Verifiers: Reward Modeling as Next-Token Prediction
  • Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, Rishabh Agarwal
• Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning
  • Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar
• Improve Mathematical Reasoning in Language Models by Automated Process Supervision
  • Liangchen Luo, Yinxiao Liu, Rosanne Liu, Samrat Phatale, Harsh Lara, Yunxuan Li, Lei Shu, Yun Zhu, Lei Meng, Jiao Sun, Abhinav Rastogi
• Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
  • Peiyi Wang, Lei Li, Zhihong Shao, R.X. Xu, Damai Dai, Yifei Li, Deli Chen, Y.Wu, Zhifang Sui
• Planning In Natural Language Improves LLM Search For Code Generation
  • Evan Wang, Federico Cassano, Catherine Wu, Yunfeng Bai, Will Song, Vaskar Nath, Ziwen Han, Sean Hendryx, Summer Yue, Hugh Zhang
• PROCESSBENCH: Identifying Process Errors in Mathematical Reasoning
  • Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin
• AFlow: Automating Agentic Workflow Generation
  • Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, Bingnan Zheng, Bang Liu, Yuyu Luo, Chenglin Wu
• Interpretable Contrastive Monte Carlo Tree Search Reasoning
  • Zitian Gao, Boye Niu, Xuzheng He, Haotian Xu, Hongzhang Liu, Aiwei Liu, Xuming Hu, Lijie Wen
• Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
  • Pranav Putta, Edmund Mills, Naman Garg, Sumeet Motwani, Chelsea Finn, Divyansh Garg, Rafael Rafailov
• Mixture-of-Agents Enhances Large Language Model Capabilities
  • Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, James Zou
• Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models
  • Zhiyuan Hu, Chumin Liu, Xidong Feng, Yilun Zhao, See-Kiong Ng, Anh Tuan Luu, Junxian He, Pang Wei Koh, Bryan Hooi
• Advancing LLM Reasoning Generalists with Preference Trees
  • Lifan Yuan, Ganqu Cui, Hanbin Wang, Ning Ding, Xingyao Wang, Jia Deng, Boji Shan et al.
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
  • Ye Tian, Baolin Peng, Linfeng Song, Lifeng Jin, Dian Yu, Haitao Mi, and Dong Yu.
• AlphaMath Almost Zero: Process Supervision Without Process
  • Guoxin Chen, Minpeng Liao, Chengxi Li, Kai Fan.
• ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search
  • Dan Zhang, Sining Zhoubian, Yisong Yue, Yuxiao Dong, and Jie Tang.
• Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
  • Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song, Haocheng Feng, Li Shen, Dacheng Tao
• Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models
  • Yuhao Dong, Zuyan Liu, Hai-Long Sun, Jingkang Yang, Winston Hu, Yongming Rao, Ziwei Liu
• MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time
  • Jikun Kang, Xin Zhe Li, Xi Chen, Amirreza Kazemi, Qianyi Sun, Boxing Chen, Dong Li, Xu He, Quan He, Feng Wen, Jianye Hao, Jun Yao.
• Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning
  • Yuxi Xie, Anirudh Goyal, Wenyue Zheng, Min-Yen Kan, Timothy P. Lillicrap, Kenji Kawaguchi, Michael Shieh.
• When is Tree Search Useful for LLM Planning? It Depends on the Discriminator
  • Ziru Chen, Michael White, Raymond Mooney, Ali Payani, Yu Su, Huan Sun
• Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
  • Zhiyuan Li, Hong Liu, Denny Zhou, Tengyu Ma.
• To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
  • Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, Dongwei Jiang, Manya Wadhwa, Prasann Singhal, Xinyu Zhao, Xi Ye, Kyle Mahowald, Greg Durrett
• Do Large Language Models Latently Perform Multi-Hop Reasoning?
  • Sohee Yang, Elena Gribovskaya, Nora Kassner, Mor Geva, Sebastian Riedel
• Chain-of-Thought Reasoning Without Prompting
  • Xuezhi Wang, Denny Zhou
• Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
  • Zhenting Qi, Mingyuan Ma, Jiahang Xu, Li Lyna Zhang, Fan Yang, Mao Yang
• Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs
  • Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin
• ReFT: Reasoning with Reinforced Fine-Tuning
  • Trung Quoc Luong, Xinbo Zhang, Zhanming Jie, Peng Sun, Xiaoran Jin, Hang Li
• VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment
  • Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron Courville, Nicolas Le Roux
• Stream of Search (SoS): Learning to Search in Language
  • Kanishk Gandhi, Denise Lee, Gabriel Grand, Muxin Liu, Winson Cheng, Archit Sharma, Noah D. Goodman
• GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models
  • Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, Mehrdad Farajtabar
• Evaluation of OpenAI o1: Opportunities and Challenges of AGI
  • Tianyang Zhong, Zhengliang Liu, Yi Pan, Yutong Zhang, Yifan Zhou, Shizhe Liang, Zihao Wu, Yanjun Lyu, Peng Shu, Xiaowei Yu, Chao Cao, Hanqi Jiang, Hanxu Chen, Yiwei Li, Junhao Chen, etc.
• Evaluating LLMs at Detecting Errors in LLM Responses
  • Ryo Kamoi, Sarkar Snigdha Sarathi Das, Renze Lou, Jihyun Janice Ahn, Yilun Zhao, Xiaoxin Lu, Nan Zhang, Yusen Zhang, Ranran Haoran Zhang, Sujeeth Reddy Vummanthala, Salika Dave, Shaobo Qin, Arman Cohan, Wenpeng Yin, Rui Zhang
• On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability
  • Kevin Wang, Junbo Li, Neel P. Bhatt, Yihan Xi, Qiang Liu, Ufuk Topcu, Zhangyang Wang
• Not All LLM Reasoners Are Created Equal
  • Arian Hosseini, Alessandro Sordoni, Daniel Toyama, Aaron Courville, Rishabh Agarwal
• LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench
  • Karthik Valmeekam, Kaya Stechly, Subbarao Kambhampati
• A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
  • Siwei Wu, Zhongyuan Peng, Xinrun Du, Tuney Zheng, Minghao Liu, Jialong Wu, Jiachen Ma, Yizhi Li, Jian Yang, Wangchunshu Zhou, Qunshu Lin, Junbo Zhao, Zhaoxiang Zhang, Wenhao Huang, Ge Zhang, Chenghua Lin, J.H. Liu
• Thinking LLMs: General Instruction Following with Thought Generation
  • Tianhao Wu, Janice Lan, Weizhe Yuan, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar
• Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning Through Trap Problems
  • Jun Zhao, Jingqi Tong, Yurong Mou, Ming Zhang, Qi Zhang, Xuanjing Huang
• V-STaR: Training Verifiers for Self-Taught Reasoners
  • Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron Courville, Alessandro Sordoni, Rishabh Agarwal
• CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks
  • Tianlong Wang, Junzhe Chen, Xueting Han, Jing Bai
• RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
  • Tianhao Wu, Janice Lan, Weizhe Yuan, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning
  • Chaojie Wang, Yanchen Deng, Zhiyi Lyu, Liang Zeng, Jujie He, Shuicheng Yan, Bo An
• Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning
  • Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, Hongsheng Li

2023

• Let's Verify Step by Step
  • Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe
• V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs
  • Penghao Wu, Saining Xie
• Training Chain-of-Thought via Latent-Variable Inference
  • Du Phan, Matthew D. Hoffman, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous
• Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training
  • Xidong Feng, Ziyu Wan, Muning Wen, Stephen Marcus McAleer, Ying Wen, Weinan Zhang, Jun Wang
• OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning
  • Fei Yu, Anningzhe Gao, Benyou Wang
• Reasoning with Language Model is Planning with World Model
  • Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, Zhiting Hu
• Don’t throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding
  • Liu, Jiacheng, Andrew Cohen, Ramakanth Pasunuru, Yejin Choi, Hannaneh Hajishirzi, and Asli Celikyilmaz.
• Certified reasoning with language models
  • Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, Noah D. Goodman
• Large Language Models Cannot Self-Correct Reasoning Yet
  • Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, Denny Zhou

2022

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  • Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou
• Self-Consistency Improves Chain of Thought Reasoning in Language Models
  • Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, Denny Zhou
• Self-critiquing models for assisting human evaluators
  • William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, Jan Leike
• Chain of Thought Imitation with Procedure Cloning
  • Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum.
• STaR: Bootstrapping Reasoning With Reasoning
  • Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. Goodman
• Solving math word problems with processand outcome-based feedback
  • Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, Irina Higgins

2021

• Training Verifiers to Solve Math Word Problems
  • Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman
• Scalable Online Planning via Reinforcement Learning Fine-Tuning
  • Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart Russell, Noam Brown.
• Scaling Scaling Laws with Board Games
  • Andy L. Jones.
• Show Your Work: Scratchpads for Intermediate Computation with Language Models
  • Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, Augustus Odena

Before 2021

• Improving Policies via Search in Cooperative Partially Observable Games
  • Adam Lerer, Hengyuan Hu, Jakob Foerster, Noam Brown.
• Generative Language Modeling for Automated Theorem Proving
  • Stanislas Polu, Ilya Sutskever
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
  • David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis.