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We introduce Phi-4-reasoning, a 14-billion parameter reasoning model that achieves strong performance on complex reasoning tasks.
Computers and intractability: a guide to the theory of np-completeness (michael r. garey and david s. johnson)
Juris Hartmanis · 1982
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Computational complexity
Christos H Papadimitriou · 2003
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Measuring massive multitask language understanding, 2021
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2009
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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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Towards accountable ai: Hybrid human-machine analyses for characterizing system failure
Besmira Nushi, Ece Kamar, and Eric Horvitz · 2018
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Designing disaggregated evaluations of ai systems: Choices, considerations, and tradeoffs
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, W Duncan Wadsworth, and Hanna Wallach · 2021
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Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu · 2021
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Overreliance on ai literature review
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Language models are multilingual chain-of-thought reasoners, 2022
Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, and Jason Wei · 2022
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Extending context window of large language models via positional interpolation
Shouyuan Chen, Sherman Wong, Liangjian Chen, and Yuandong Tian · 2023
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Scaling laws for reward model overoptimization
Leo Gao, John Schulman, and Jacob Hilton · 2023
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Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, et al · 2023
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Phi-2: The surprising power of small language models
Mojan Javaheripi, Sébastien Bubeck, Marah Abdin, Jyoti Aneja, Caio César Teodoro Mendes, Weizhu Chen, Allie Del Giorno, Ronen Eldan, Sivakanth Gopi, Suriya Gunasekar, Piero Kauffmann, Yin Tat Lee, Yuanzhi Li, Anh Nguyen, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Michael Santacroce, Harkirat Singh Behl, Adam Taumann Kalai, Xin Wang, Rachel Ward, Philipp Witte, Cyril Zhang, and Yi Zhang · 2023
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Functional interpolation for relative positions improves long context transformers
Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontanon, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, and Srinadh Bhojanapalli · 2023
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Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang · 2023
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A framework for automated measurement of responsible ai harms in generative ai applications, 2023
Ahmed Magooda, Alec Helyar, Kyle Jackson, David Sullivan, Chad Atalla, Emily Sheng, Dan Vann, Richard Edgar, Hamid Palangi, Roman Lutz, Hongliang Kong, Vincent Yun, Eslam Kamal, Federico Zarfati, Hanna Wallach, Sarah Bird, and Mei Chen · 2023
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Orca 2: Teaching small language models how to reason
Arindam Mitra, Luciano Del Corro, Shweti Mahajan, Andres Codas, Clarisse Simoes, Sahaj Agarwal, Xuxi Chen, Anastasia Razdaibiedina, Erik Jones, Kriti Aggarwal, et al · 2023
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Orca: Progressive learning from complex explanation traces of gpt-4
Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awadallah · 2023
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Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman · 2023
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Instruction-following evaluation for large language models
Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou · 2023
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Eureka: Evaluating and understanding large foundation models
Vidhisha Balachandran, Jingya Chen, Neel Joshi, Besmira Nushi, Hamid Palangi, Eduardo Salinas, Vibhav Vineet, James Woffinden-Luey, and Safoora Yousefi · 2024
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Benchagents: Automated benchmark creation with agent interaction
Natasha Butt, Varun Chandrasekaran, Neel Joshi, Besmira Nushi, and Vidhisha Balachandran · 2024
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Matharena: Evaluating llms on uncontaminated math competitions, February 2025
Mislav Balunović, Jasper Dekoninck, Ivo Petrov, Nikola Jovanović, and Martin Vechev · 2025
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Reinforcement learning for reasoning in small llms: What works and what doesn’t, 2025
Quy-Anh Dang and Chris Ngo · 2025
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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, et al · 2025
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Gemini flash thinking
Google · 2025
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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, and Mao Yang · 2025
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Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al · 2024
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Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al · 2024
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Same task, more tokens: the impact of input length on the reasoning performance of large language models
Mosh Levy, Alon Jacoby, and Yoav Goldberg · 2024
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From crowdsourced data to high-quality benchmarks: Arena-hard and benchbuilder pipeline
Tianle Li, Wei-Lin Chiang, Evan Frick, Lisa Dunlap, Tianhao Wu, Banghua Zhu, Joseph E Gonzalez, and Ion Stoica · 2024
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Agentinstruct: Toward generative teaching with agentic flows
Arindam Mitra, Luciano Del Corro, Guoqing Zheng, Shweti Mahajan, Dany Rouhana, Andres Codas, Yadong Lu, Wei-ge Chen, Olga Vrousgos, Corby Rosset, et al · 2024
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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 · 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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Hybridflow: A flexible and efficient rlhf framework
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Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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A sober look at progress in language model reasoning: Pitfalls and paths to reproducibility
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Exaone deep: Reasoning enhanced language models
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Limr: Less is more for rl scaling, 2025
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Unearthing skill-level insights for understanding trade-offs of foundation models
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Openai o3-mini system card
OpenAI · 2025
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Proof or bluff? evaluating llms on 2025 usa math olympiad
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Kimi k1. 5: Scaling reinforcement learning with llms
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Guanghao Ye, Khiem Duc Pham, Xinzhi Zhang, Sivakanth Gopi, Baolin Peng, Beibin Li, Janardhan Kulkarni, and Huseyin A Inan · 2025
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Demystifying long chain-of-thought reasoning in llms
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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
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