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Process Reward Models (PRMs) have shown promise in enhancing the mathematical reasoning capabilities of Large Language Models (LLMs) through Test-Time Scaling (TTS).
Adam: A method for stochastic optimization
P Kingma Diederik · 2014
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Trl: Transformer reinforcement learning
Leandro von Werra, Younes Belkada, Lewis Tunstall, Edward Beeching, Tristan Thrush, Nathan Lambert, Shengyi Huang, Kashif Rasul, and Quentin Gallouédec · 2020
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Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric Xing, and Liang Lin · 2021
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Self-consistency improves chain of thought reasoning in language models
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Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
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Sigmoid loss for language image pre-training
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Gemini: a family of highly capable multimodal models
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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 · 2023
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Critic: Large language models can self-correct with tool-interactive critiquing
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Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond
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Pytorch FSDP: experiences on scaling fully sharded data parallel
Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Pritam Damania, Bernard Nguyen, Geeta Chauhan, Yuchen Hao, Ajit Mathews, and Shen Li · 2023
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Efficient memory management for large language model serving with pagedattention
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Internlm-math: Open math large language models toward verifiable reasoning
Huaiyuan Ying, Shuo Zhang, Linyang Li, Zhejian Zhou, Yunfan Shao, Zhaoye Fei, Yichuan Ma, Jiawei Hong, Kuikun Liu, Ziyi Wang, Yudong Wang, Zijian Wu, Shuaibin Li, Fengzhe Zhou, Hongwei Liu, Songyang Zhang, Wenwei Zhang, Hang Yan, Xipeng Qiu, Jiayu Wang, Kai Chen, and Dahua Lin · 2024
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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, YK Li, Y Wu, et al · 2024
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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
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Exploring the mystery of influential data for mathematical reasoning
Xinzhe Ni, Yeyun Gong, Zhibin Gou, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen · 2024
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Atomthink: A slow thinking framework for multimodal mathematical reasoning
Kun Xiang, Zhili Liu, Zihao Jiang, Yunshuang Nie, Runhui Huang, Haoxiang Fan, Hanhui Li, Weiran Huang, Yihan Zeng, Jianhua Han, et al · 2024
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Infimm-webmath-40b: Advancing multimodal pre-training for enhanced mathematical reasoning
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Math-llava: Bootstrapping mathematical reasoning for multimodal large language models
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Geogpt4v: Towards geometric multi-modal large language models with geometric image generation
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Linger Deng, Yuliang Liu, Bohan Li, Dongliang Luo, Liang Wu, Chengquan Zhang, Pengyuan Lyu, Ziyang Zhang, Gang Zhang, Errui Ding, et al · 2024
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Multimodal chain-of-thought reasoning in language models
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Multimath: Bridging visual and mathematical reasoning for large language models
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Rest-mcts*: LLM self-training via process reward guided tree search
Dan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue, Yuxiao Dong, and Jie Tang · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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B-star: Monitoring and balancing exploration and exploitation in self-taught reasoners
Weihao Zeng, Yuzhen Huang, Lulu Zhao, Yijun Wang, Zifei Shan, and Junxian He · 2024
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Math-puma: Progressive upward multimodal alignment to enhance mathematical reasoning
Wenwen Zhuang, Xin Huang, Xiantao Zhang, and Jin Zeng · 2025
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Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model?
Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Yang Yue, Shiji Song, and Gao Huang · 2025
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Reward hacking and how to mitigate it
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Llava-onevision: Easy visual task transfer
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Deepseek-vl: towards real-world vision-language understanding
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Solving olympiad geometry without human demonstrations
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Improve mathematical reasoning in language models by automated process supervision
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Thinking before looking: Improving multimodal llm reasoning via mitigating visual hallucination
Haojie Zheng, Tianyang Xu, Hanchi Sun, Shu Pu, Ruoxi Chen, and Lichao Sun · 2024
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Process reward model with q-value rankings
Wendi Li and Yixuan Li · 2024
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Reward shaping to mitigate reward hacking in RLHF
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
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What are step-level reward models rewarding? counterintuitive findings from mcts-boosted mathematical reasoning
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Llama 3.2: Revolutionizing edge AI and vision with open, customizable models — ai.meta.com
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Llamav-o1: Rethinking step-by-step visual reasoning in llms
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Vilbench: A suite for vision-language process reward modeling
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Visualprm: An effective process reward model for multimodal reasoning
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Mm-eureka: Exploring visual aha moment with rule-based large-scale reinforcement learning
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Open r1: A fully open reproduction of deepseek-r1, January 2025
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