Fetching the paper…
Reading the bibliography…
We present VAPO, Value-based Augmented Proximal Policy Optimization framework for reasoning models., a novel framework tailored for reasoning models within the value-based paradigm.
Reporting explained variance
Ron Good and Harold J. Fletcher · 1981
Earlier work this paper cites.
Reinforcement learning: An introduction , volume 1
Richard S Sutton, Andrew G Barto, et al · 1998
Earlier work this paper cites.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Self-imitation learning
Junhyuk Oh, Yijie Guo, Satinder Singh, and Honglak Lee · 2018
Earlier work this paper cites.
Buy 4 REINFORCE samples, get a baseline for free!
Wouter Kool, Herke van Hoof, and Max Welling · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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
Earlier work this paper cites.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2023
Earlier work this paper cites.
OpenAI · 2023
Earlier work this paper cites.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Cited alongside, same era.
Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, and Sara Hooker · 2024
Cited alongside, same era.
Claude 3.5 sonnet, 2024
Anthropic · 2024
Cited alongside, same era.
Gemini 2.0 flash thinking, 2024
Google DeepMind · 2024
Cited alongside, same era.
Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al · 2024
Cited alongside, same era.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025
DeepSeek-AI · 2025
Closest in time.
Reinforce++: A simple and efficient approach for aligning large language models
Jian Hu · 2025
Closest in time.
Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, Xiangyu Zhang, and Heung-Yeung Shum · 2025
Closest in time.
Understanding r1-zero-like training: A critical perspective, 2025
Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin · 2025
Closest in time.
Real: Efficient rlhf training of large language models with parameter reallocation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning to reason with llms, 2024
OpenAI · 2024
Cited alongside, same era.
Qwq-32b: Embracing the power of reinforcement learning, 2024
Qwen · 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, Mingchuan Zhang, YK Li, Yu Wu, and Daya Guo · 2024
Cited alongside, same era.
Grok 3 beta — the age of reasoning agents, 2024
XAI · 2024
Cited alongside, same era.
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
Cited in the paper.
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
Cited in the paper.
Zhiyu Mei, Wei Fu, Kaiwei Li, Guangju Wang, Huanchen Zhang, and Yi Wu · 2025
Closest in time.
Exploring data scaling trends and effects in reinforcement learning from human feedback
Wei Shen, Guanlin Liu, Zheng Wu, Ruofei Zhu, Qingping Yang, Chao Xin, Yu Yue, and Lin Yan · 2025
Closest in time.
Kimi k1. 5: Scaling reinforcement learning with llms
Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, et al · 2025
Closest in time.
Dapo: An open-source llm reinforcement learning system at scale, 2025
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, and Mingxuan Wang · 2025
Closest in time.
What’s behind ppo’s collapse in long-cot? value optimization holds the secret, 2025
Yufeng Yuan, Yu Yue, Ruofei Zhu, Tiantian Fan, and Lin Yan · 2025
Closest in time.