Fetching the paper…
Reading the bibliography…
This paper introduces Light-R1, an open-source suite for training long reasoning models using reproducible and cost-effective methodology.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2015 · 2015
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022
Earlier work this paper cites.
Scaling laws for reward model overoptimization
Leo Gao, John Schulman, and Jacob Hilton. 2023 · 2023
Earlier work this paper cites.
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023 · 2023
Earlier work this paper cites.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. 2023 · 2023
Earlier work this paper cites.
Gpqa: A graduate-level google-proof q&a benchmark
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. 2023 · 2023
Earlier work this paper cites.
Ties-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal. 2023 · 2023
Earlier work this paper cites.
Noise contrastive alignment of language models with explicit rewards
Huayu Chen, Guande He, Hang Su, and Jun Zhu. 2024 · 2024
Earlier work this paper cites.
Omni-math: A universal olympiad level mathematic benchmark for large language models
Bofei Gao, Feifan Song, Zhe Yang, Zefan Cai, Yibo Miao, Qingxiu Dong, Lei Li, Chenghao Ma, Liang Chen, Runxin Xu, Zhengyang Tang, Benyou Wang, Daoguang Zan, Shanghaoran Quan, Ge Zhang, Lei Sha, Yichang Zhang, Xuancheng Ren, Tianyu Liu, and Baobao Chang. 2024 · 2024
Earlier work this paper cites.
Arcee’s MergeKit: A toolkit for merging large language models
Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, Luke Meyers, Vladimir Karpukhin, Brian Benedict, Mark McQuade, and Jacob Solawetz. 2024 · 2024
Earlier work this paper cites.
American invitational mathematics examination - aime
MAA. 2024 · 2024
Earlier work this paper cites.
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, and Ji-Rong Wen. 2024 · 2024
Earlier work this paper cites.
Learning to reason with llms
OpenAI. 2024 · 2024
Cited alongside, same era.
Qwen2.5: A party of foundation models
Qwen. 2024 · 2024
Cited alongside, same era.
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y. K. Li, Y. Wu, and Daya Guo. 2024 · 2024
Cited alongside, same era.
Hybridflow: A flexible and efficient rlhf framework
Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu. 2024 · 2024
Cited alongside, same era.
The good, the bad, and the greedy: Evaluation of llms should not ignore non-determinism
Yifan Song, Guoyin Wang, Sujian Li, and Bill Yuchen Lin. 2024 · 2024
Cited alongside, same era.
Kimi k1.5: Scaling reinforcement learning with llms
Kimi. 2025 · 2025
Closest in time.
Bespoke-stratos: The unreasonable effectiveness of reasoning distillation
Bespoke Labs. 2025 · 2025
Closest in time.
Limr: Less is more for rl scaling
Xuefeng Li, Haoyang Zou, and Pengfei Liu. 2025 · 2025
Closest in time.
There may not be aha moment in r1-zero-like training — a pilot study
Zichen Liu, Changyu Chen, Wenjun Li, Tianyu Pang, Chao Du, and Min Lin. 2025 · 2025
Closest in time.
MiniMax-01: Scaling Foundation Models with Lightning Attention
MiniMax. 2025 · 2025
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Shubham Toshniwal, Wei Du, Ivan Moshkov, Branislav Kisacanin, Alexan Ayrapetyan, and Igor Gitman. 2024 · 2024
Cited alongside, same era.
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, and Zhifang Sui. 2024 · 2024
Cited alongside, same era.
Training large language models for reasoning through reverse curriculum reinforcement learning
Zhiheng Xi, Wenxiang Chen, Boyang Hong, Senjie Jin, Rui Zheng, Wei He, Yiwen Ding, Shichun Liu, Xin Guo, Junzhe Wang, Honglin Guo, Wei Shen, Xiaoran Fan, Yuhao Zhou, Shihan Dou, Xiao Wang, Xinbo Zhang, Peng Sun, Tao Gui, Qi Zhang, and Xuanjing Huang. 2024 · 2024
Cited alongside, same era.
Metamath: Bootstrap your own mathematical questions for large language models
Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. 2024 · 2024
Cited alongside, same era.
360-llama-factory
Haosheng Zou, Xiaowei Lv, Shousheng Jia, and Xiangzheng Zhang. 2024 · 2024
Cited alongside, same era.
Big-math: A large-scale, high-quality math dataset for reinforcement learning in language models
Alon Albalak, Duy Phung, Nathan Lile, Rafael Rafailov, Kanishk Gandhi, Louis Castricato, Anikait Singh, Chase Blagden, Violet Xiang, Dakota Mahan, and Nick Haber. 2025 · 2025
Cited alongside, same era.
Process Reinforcement through Implicit Rewards
Ganqu Cui, Lifan Yuan, Zefan Wang, Hanbin Wang, Wendi Li, Bingxiang He, Yuchen Fan, Tianyu Yu, Qixin Xu, Weize Chen, Jiarui Yuan, Huayu Chen, Kaiyan Zhang, Xingtai Lv, Shuo Wang, Yuan Yao, Xu Han, Hao Peng, Yu Cheng, Zhiyuan Liu, Maosong Sun, Bowen Zhou, and Ning Ding. 2025 · 2025
Cited alongside, same era.
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. 2025 · 2025
Closest in time.
Open r1: A fully open reproduction of deepseek-r1
OpenR1. 2025 · 2025
Closest in time.
Open Thoughts
OpenThoughts. 2025 · 2025
Closest in time.
Qwq-32b: Embracing the power of reinforcement learning
Qwen. 2025 · 2025
Closest in time.
Redstar: Does scaling long-cot data unlock better slow-reasoning systems?
Haotian Xu, Xing Wu, Weinong Wang, Zhongzhi Li, Da Zheng, Boyuan Chen, Yi Hu, Shijia Kang, Jiaming Ji, Yingying Zhang, Zhijiang Guo, Yaodong Yang, Muhan Zhang, and Debing Zhang. 2025 · 2025
Closest in time.
Limo: Less is more for reasoning
Yixin Ye, Zhen Huang, Yang Xiao, Ethan Chern, Shijie Xia, and Pengfei Liu. 2025 · 2025
Closest in time.
Demystifying Long Chain-of-Thought Reasoning in LLMs
Edward Yeo, Yuxuan Tong, Morry Niu, Graham Neubig, and Xiang Yue. 2025 · 2025
Closest in time.
7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient
Weihao Zeng, Yuzhen Huang, Wei Liu, Keqing He, Qian Liu, Zejun Ma, and Junxian He. 2025 · 2025
Closest in time.
Online-dpo-r1: Unlocking effective reasoning without the ppo overhead
Hanning Zhang, Jiarui Yao, Chenlu Ye, Wei Xiong, and Tong Zhang. 2025 · 2025
Closest in time.