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1. Competitive Programming with Large Reasoning Models

Author

OpenAI, El-Kishky, Ahmed, Wei, Alexander, Saraiva, Andre, Minaiev, Borys, Selsam, Daniel, Dohan, David, Song, Francis, Lightman, Hunter, Clavera, Ignasi, Pachocki, Jakub, Tworek, Jerry, Kuhn, Lorenz, Kaiser, Lukasz, Chen, Mark, Schwarzer, Max, Rohaninejad, Mostafa, McAleese, Nat, contributors, o3, Mürk, Oleg, Garg, Rhythm, Shu, Rui, Sidor, Szymon, Kosaraju, Vineet, and Zhou, Wenda

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

We show that reinforcement learning applied to large language models (LLMs) significantly boosts performance on complex coding and reasoning tasks. Additionally, we compare two general-purpose reasoning models - OpenAI o1 and an early checkpoint of o3 - with a domain-specific system, o1-ioi, which uses hand-engineered inference strategies designed for competing in the 2024 International Olympiad in Informatics (IOI). We competed live at IOI 2024 with o1-ioi and, using hand-crafted test-time strategies, placed in the 49th percentile. Under relaxed competition constraints, o1-ioi achieved a gold medal. However, when evaluating later models such as o3, we find that o3 achieves gold without hand-crafted domain-specific strategies or relaxed constraints. Our findings show that although specialized pipelines such as o1-ioi yield solid improvements, the scaled-up, general-purpose o3 model surpasses those results without relying on hand-crafted inference heuristics. Notably, o3 achieves a gold medal at the 2024 IOI and obtains a Codeforces rating on par with elite human competitors. Overall, these results indicate that scaling general-purpose reinforcement learning, rather than relying on domain-specific techniques, offers a robust path toward state-of-the-art AI in reasoning domains, such as competitive programming.

Published
2025

2. OpenAI o1 System Card

Author

OpenAI, Jaech, Aaron, Kalai, Adam, Lerer, Adam, Richardson, Adam, El-Kishky, Ahmed, Low, Aiden, Helyar, Alec, Madry, Aleksander, Beutel, Alex, Carney, Alex, Iftimie, Alex, Karpenko, Alex, Passos, Alex Tachard, Neitz, Alexander, Prokofiev, Alexander, Wei, Alexander, Tam, Allison, Bennett, Ally, Kumar, Ananya, Saraiva, Andre, Vallone, Andrea, Duberstein, Andrew, Kondrich, Andrew, Mishchenko, Andrey, Applebaum, Andy, Jiang, Angela, Nair, Ashvin, Zoph, Barret, Ghorbani, Behrooz, Rossen, Ben, Sokolowsky, Benjamin, Barak, Boaz, McGrew, Bob, Minaiev, Borys, Hao, Botao, Baker, Bowen, Houghton, Brandon, McKinzie, Brandon, Eastman, Brydon, Lugaresi, Camillo, Bassin, Cary, Hudson, Cary, Li, Chak Ming, de Bourcy, Charles, Voss, Chelsea, Shen, Chen, Zhang, Chong, Koch, Chris, Orsinger, Chris, Hesse, Christopher, Fischer, Claudia, Chan, Clive, Roberts, Dan, Kappler, Daniel, Levy, Daniel, Selsam, Daniel, Dohan, David, Farhi, David, Mely, David, Robinson, David, Tsipras, Dimitris, Li, Doug, Oprica, Dragos, Freeman, Eben, Zhang, Eddie, Wong, Edmund, Proehl, Elizabeth, Cheung, Enoch, Mitchell, Eric, Wallace, Eric, Ritter, Erik, Mays, Evan, Wang, Fan, Such, Felipe Petroski, Raso, Filippo, Leoni, Florencia, Tsimpourlas, Foivos, Song, Francis, von Lohmann, Fred, Sulit, Freddie, Salmon, Geoff, Parascandolo, Giambattista, Chabot, Gildas, Zhao, Grace, Brockman, Greg, Leclerc, Guillaume, Salman, Hadi, Bao, Haiming, Sheng, Hao, Andrin, Hart, Bagherinezhad, Hessam, Ren, Hongyu, Lightman, Hunter, Chung, Hyung Won, Kivlichan, Ian, O'Connell, Ian, Osband, Ian, Gilaberte, Ignasi Clavera, Akkaya, Ilge, Kostrikov, Ilya, Sutskever, Ilya, Kofman, Irina, Pachocki, Jakub, Lennon, James, Wei, Jason, Harb, Jean, Twore, Jerry, Feng, Jiacheng, Yu, Jiahui, Weng, Jiayi, Tang, Jie, Yu, Jieqi, Candela, Joaquin Quiñonero, Palermo, Joe, Parish, Joel, Heidecke, Johannes, Hallman, John, Rizzo, John, Gordon, Jonathan, Uesato, Jonathan, Ward, Jonathan, Huizinga, Joost, Wang, Julie, Chen, Kai, Xiao, Kai, Singhal, Karan, Nguyen, Karina, Cobbe, Karl, Shi, Katy, Wood, Kayla, Rimbach, Kendra, Gu-Lemberg, Keren, Liu, Kevin, Lu, Kevin, Stone, Kevin, Yu, Kevin, Ahmad, Lama, Yang, Lauren, Liu, Leo, Maksin, Leon, Ho, Leyton, Fedus, Liam, Weng, Lilian, Li, Linden, McCallum, Lindsay, Held, Lindsey, Kuhn, Lorenz, Kondraciuk, Lukas, Kaiser, Lukasz, Metz, Luke, Boyd, Madelaine, Trebacz, Maja, Joglekar, Manas, Chen, Mark, Tintor, Marko, Meyer, Mason, Jones, Matt, Kaufer, Matt, Schwarzer, Max, Shah, Meghan, Yatbaz, Mehmet, Guan, Melody Y., Xu, Mengyuan, Yan, Mengyuan, Glaese, Mia, Chen, Mianna, Lampe, Michael, Malek, Michael, Wang, Michele, Fradin, Michelle, McClay, Mike, Pavlov, Mikhail, Wang, Miles, Wang, Mingxuan, Murati, Mira, Bavarian, Mo, Rohaninejad, Mostafa, McAleese, Nat, Chowdhury, Neil, Ryder, Nick, Tezak, Nikolas, Brown, Noam, Nachum, Ofir, Boiko, Oleg, Murk, Oleg, Watkins, Olivia, Chao, Patrick, Ashbourne, Paul, Izmailov, Pavel, Zhokhov, Peter, Dias, Rachel, Arora, Rahul, Lin, Randall, Lopes, Rapha Gontijo, Gaon, Raz, Miyara, Reah, Leike, Reimar, Hwang, Renny, Garg, Rhythm, Brown, Robin, James, Roshan, Shu, Rui, Cheu, Ryan, Greene, Ryan, Jain, Saachi, Altman, Sam, Toizer, Sam, Toyer, Sam, Miserendino, Samuel, Agarwal, Sandhini, Hernandez, Santiago, Baker, Sasha, McKinney, Scott, Yan, Scottie, Zhao, Shengjia, Hu, Shengli, Santurkar, Shibani, Chaudhuri, Shraman Ray, Zhang, Shuyuan, Fu, Siyuan, Papay, Spencer, Lin, Steph, Balaji, Suchir, Sanjeev, Suvansh, Sidor, Szymon, Broda, Tal, Clark, Aidan, Wang, Tao, Gordon, Taylor, Sanders, Ted, Patwardhan, Tejal, Sottiaux, Thibault, Degry, Thomas, Dimson, Thomas, Zheng, Tianhao, Garipov, Timur, Stasi, Tom, Bansal, Trapit, Creech, Trevor, Peterson, Troy, Eloundou, Tyna, Qi, Valerie, Kosaraju, Vineet, Monaco, Vinnie, Pong, Vitchyr, Fomenko, Vlad, Zheng, Weiyi, Zhou, Wenda, McCabe, Wes, Zaremba, Wojciech, Dubois, Yann, Lu, Yinghai, Chen, Yining, Cha, Young, Bai, Yu, He, Yuchen, Zhang, Yuchen, Wang, Yunyun, Shao, Zheng, and Li, Zhuohan

Subjects
Computer Science - Artificial Intelligence
Abstract

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

Published
2024

3. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Author

Srivastava, Aarohi, Rastogi, Abhinav, Rao, Abhishek, Shoeb, Abu Awal Md, Abid, Abubakar, Fisch, Adam, Brown, Adam R., Santoro, Adam, Gupta, Aditya, Garriga-Alonso, Adrià, Kluska, Agnieszka, Lewkowycz, Aitor, Agarwal, Akshat, Power, Alethea, Ray, Alex, Warstadt, Alex, Kocurek, Alexander W., Safaya, Ali, Tazarv, Ali, Xiang, Alice, Parrish, Alicia, Nie, Allen, Hussain, Aman, Askell, Amanda, Dsouza, Amanda, Slone, Ambrose, Rahane, Ameet, Iyer, Anantharaman S., Andreassen, Anders, Madotto, Andrea, Santilli, Andrea, Stuhlmüller, Andreas, Dai, Andrew, La, Andrew, Lampinen, Andrew, Zou, Andy, Jiang, Angela, Chen, Angelica, Vuong, Anh, Gupta, Animesh, Gottardi, Anna, Norelli, Antonio, Venkatesh, Anu, Gholamidavoodi, Arash, Tabassum, Arfa, Menezes, Arul, Kirubarajan, Arun, Mullokandov, Asher, Sabharwal, Ashish, Herrick, Austin, Efrat, Avia, Erdem, Aykut, Karakaş, Ayla, Roberts, B. Ryan, Loe, Bao Sheng, Zoph, Barret, Bojanowski, Bartłomiej, Özyurt, Batuhan, Hedayatnia, Behnam, Neyshabur, Behnam, Inden, Benjamin, Stein, Benno, Ekmekci, Berk, Lin, Bill Yuchen, Howald, Blake, Orinion, Bryan, Diao, Cameron, Dour, Cameron, Stinson, Catherine, Argueta, Cedrick, Ramírez, César Ferri, Singh, Chandan, Rathkopf, Charles, Meng, Chenlin, Baral, Chitta, Wu, Chiyu, Callison-Burch, Chris, Waites, Chris, Voigt, Christian, Manning, Christopher D., Potts, Christopher, Ramirez, Cindy, Rivera, Clara E., Siro, Clemencia, Raffel, Colin, Ashcraft, Courtney, Garbacea, Cristina, Sileo, Damien, Garrette, Dan, Hendrycks, Dan, Kilman, Dan, Roth, Dan, Freeman, Daniel, Khashabi, Daniel, Levy, Daniel, González, Daniel Moseguí, Perszyk, Danielle, Hernandez, Danny, Chen, Danqi, Ippolito, Daphne, Gilboa, Dar, Dohan, David, Drakard, David, Jurgens, David, Datta, Debajyoti, Ganguli, Deep, Emelin, Denis, Kleyko, Denis, Yuret, Deniz, Chen, Derek, Tam, Derek, Hupkes, Dieuwke, Misra, Diganta, Buzan, Dilyar, Mollo, Dimitri Coelho, Yang, Diyi, Lee, Dong-Ho, Schrader, Dylan, Shutova, Ekaterina, Cubuk, Ekin Dogus, Segal, Elad, Hagerman, Eleanor, Barnes, Elizabeth, Donoway, Elizabeth, Pavlick, Ellie, Rodola, Emanuele, Lam, Emma, Chu, Eric, Tang, Eric, Erdem, Erkut, Chang, Ernie, Chi, Ethan A., Dyer, Ethan, Jerzak, Ethan, Kim, Ethan, Manyasi, Eunice Engefu, Zheltonozhskii, Evgenii, Xia, Fanyue, Siar, Fatemeh, Martínez-Plumed, Fernando, Happé, Francesca, Chollet, Francois, Rong, Frieda, Mishra, Gaurav, Winata, Genta Indra, de Melo, Gerard, Kruszewski, Germán, Parascandolo, Giambattista, Mariani, Giorgio, Wang, Gloria, Jaimovitch-López, Gonzalo, Betz, Gregor, Gur-Ari, Guy, Galijasevic, Hana, Kim, Hannah, Rashkin, Hannah, Hajishirzi, Hannaneh, Mehta, Harsh, Bogar, Hayden, Shevlin, Henry, Schütze, Hinrich, Yakura, Hiromu, Zhang, Hongming, Wong, Hugh Mee, Ng, Ian, Noble, Isaac, Jumelet, Jaap, Geissinger, Jack, Kernion, Jackson, Hilton, Jacob, Lee, Jaehoon, Fisac, Jaime Fernández, Simon, James B., Koppel, James, Zheng, James, Zou, James, Kocoń, Jan, Thompson, Jana, Wingfield, Janelle, Kaplan, Jared, Radom, Jarema, Sohl-Dickstein, Jascha, Phang, Jason, Wei, Jason, Yosinski, Jason, Novikova, Jekaterina, Bosscher, Jelle, Marsh, Jennifer, Kim, Jeremy, Taal, Jeroen, Engel, Jesse, Alabi, Jesujoba, Xu, Jiacheng, Song, Jiaming, Tang, Jillian, Waweru, Joan, Burden, John, Miller, John, Balis, John U., Batchelder, Jonathan, Berant, Jonathan, Frohberg, Jörg, Rozen, Jos, Hernandez-Orallo, Jose, Boudeman, Joseph, Guerr, Joseph, Jones, Joseph, Tenenbaum, Joshua B., Rule, Joshua S., Chua, Joyce, Kanclerz, Kamil, Livescu, Karen, Krauth, Karl, Gopalakrishnan, Karthik, Ignatyeva, Katerina, Markert, Katja, Dhole, Kaustubh D., Gimpel, Kevin, Omondi, Kevin, Mathewson, Kory, Chiafullo, Kristen, Shkaruta, Ksenia, Shridhar, Kumar, McDonell, Kyle, Richardson, Kyle, Reynolds, Laria, Gao, Leo, Zhang, Li, Dugan, Liam, Qin, Lianhui, Contreras-Ochando, Lidia, Morency, Louis-Philippe, Moschella, Luca, Lam, Lucas, Noble, Lucy, Schmidt, Ludwig, He, Luheng, Colón, Luis Oliveros, Metz, Luke, Şenel, Lütfi Kerem, Bosma, Maarten, Sap, Maarten, ter Hoeve, Maartje, Farooqi, Maheen, Faruqui, Manaal, Mazeika, Mantas, Baturan, Marco, Marelli, Marco, Maru, Marco, Quintana, Maria Jose Ramírez, Tolkiehn, Marie, Giulianelli, Mario, Lewis, Martha, Potthast, Martin, Leavitt, Matthew L., Hagen, Matthias, Schubert, Mátyás, Baitemirova, Medina Orduna, Arnaud, Melody, McElrath, Melvin, Yee, Michael A., Cohen, Michael, Gu, Michael, Ivanitskiy, Michael, Starritt, Michael, Strube, Michael, Swędrowski, Michał, Bevilacqua, Michele, Yasunaga, Michihiro, Kale, Mihir, Cain, Mike, Xu, Mimee, Suzgun, Mirac, Walker, Mitch, Tiwari, Mo, Bansal, Mohit, Aminnaseri, Moin, Geva, Mor, Gheini, Mozhdeh, T, Mukund Varma, Peng, Nanyun, Chi, Nathan A., Lee, Nayeon, Krakover, Neta Gur-Ari, Cameron, Nicholas, Roberts, Nicholas, Doiron, Nick, Martinez, Nicole, Nangia, Nikita, Deckers, Niklas, Muennighoff, Niklas, Keskar, Nitish Shirish, Iyer, Niveditha S., Constant, Noah, Fiedel, Noah, Wen, Nuan, Zhang, Oliver, Agha, Omar, Elbaghdadi, Omar, Levy, Omer, Evans, Owain, Casares, Pablo Antonio Moreno, Doshi, Parth, Fung, Pascale, Liang, Paul Pu, Vicol, Paul, Alipoormolabashi, Pegah, Liao, Peiyuan, Liang, Percy, Chang, Peter, Eckersley, Peter, Htut, Phu Mon, Hwang, Pinyu, Miłkowski, Piotr, Patil, Piyush, Pezeshkpour, Pouya, Oli, Priti, Mei, Qiaozhu, Lyu, Qing, Chen, Qinlang, Banjade, Rabin, Rudolph, Rachel Etta, Gabriel, Raefer, Habacker, Rahel, Risco, Ramon, Millière, Raphaël, Garg, Rhythm, Barnes, Richard, Saurous, Rif A., Arakawa, Riku, Raymaekers, Robbe, Frank, Robert, Sikand, Rohan, Novak, Roman, Sitelew, Roman, LeBras, Ronan, Liu, Rosanne, Jacobs, Rowan, Zhang, Rui, Salakhutdinov, Ruslan, Chi, Ryan, Lee, Ryan, Stovall, Ryan, Teehan, Ryan, Yang, Rylan, Singh, Sahib, Mohammad, Saif M., Anand, Sajant, Dillavou, Sam, Shleifer, Sam, Wiseman, Sam, Gruetter, Samuel, Bowman, Samuel R., Schoenholz, Samuel S., Han, Sanghyun, Kwatra, Sanjeev, Rous, Sarah A., Ghazarian, Sarik, Ghosh, Sayan, Casey, Sean, Bischoff, Sebastian, Gehrmann, Sebastian, Schuster, Sebastian, Sadeghi, Sepideh, Hamdan, Shadi, Zhou, Sharon, Srivastava, Shashank, Shi, Sherry, Singh, Shikhar, Asaadi, Shima, Gu, Shixiang Shane, Pachchigar, Shubh, Toshniwal, Shubham, Upadhyay, Shyam, Shyamolima, Debnath, Shakeri, Siamak, Thormeyer, Simon, Melzi, Simone, Reddy, Siva, Makini, Sneha Priscilla, Lee, Soo-Hwan, Torene, Spencer, Hatwar, Sriharsha, Dehaene, Stanislas, Divic, Stefan, Ermon, Stefano, Biderman, Stella, Lin, Stephanie, Prasad, Stephen, Piantadosi, Steven T., Shieber, Stuart M., Misherghi, Summer, Kiritchenko, Svetlana, Mishra, Swaroop, Linzen, Tal, Schuster, Tal, Li, Tao, Yu, Tao, Ali, Tariq, Hashimoto, Tatsu, Wu, Te-Lin, Desbordes, Théo, Rothschild, Theodore, Phan, Thomas, Wang, Tianle, Nkinyili, Tiberius, Schick, Timo, Kornev, Timofei, Tunduny, Titus, Gerstenberg, Tobias, Chang, Trenton, Neeraj, Trishala, Khot, Tushar, Shultz, Tyler, Shaham, Uri, Misra, Vedant, Demberg, Vera, Nyamai, Victoria, Raunak, Vikas, Ramasesh, Vinay, Prabhu, Vinay Uday, Padmakumar, Vishakh, Srikumar, Vivek, Fedus, William, Saunders, William, Zhang, William, Vossen, Wout, Ren, Xiang, Tong, Xiaoyu, Zhao, Xinran, Wu, Xinyi, Shen, Xudong, Yaghoobzadeh, Yadollah, Lakretz, Yair, Song, Yangqiu, Bahri, Yasaman, Choi, Yejin, Yang, Yichi, Hao, Yiding, Chen, Yifu, Belinkov, Yonatan, Hou, Yu, Hou, Yufang, Bai, Yuntao, Seid, Zachary, Zhao, Zhuoye, Wang, Zijian, Wang, Zijie J., Wang, Zirui, and Wu, Ziyi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting., Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench

Published
2022

4. Physico–chemical, thermal and pasting properties of fractions obtained during three successive reduction milling of different corn types

Author

Singh, Narpinder, Bedi, Richa, Garg, Rhythm, Garg, Mukti, and Singh, Jaghmohan

Subjects
*FOOD chemistry, *INDUSTRIAL chemistry, *SANITARY chemistry, *CORN
Abstract

Abstract: The physico–chemical, thermal and pasting properties of fractions obtained during three successive reduction milling of degermed grains of four corn types (popcorn, sweet corn, dent corn, and white corn) was studied. Popcorn and white corn gave higher coarser fraction (14–20mesh) yield than dent and sweet corn. Protein content progressively decreased with increase in fineness in the particle size. Fractions of first reduction stage showed higher fat content and L ∗ value; and lower b ∗ value than the fractions from second and third reduction stages. L ∗ value increased whereas ‘a ∗’ and ‘b ∗’ values decreased with increase in fineness of the fractions. T o, T p and ΔH gel, peak viscosity, breakdown, setback and final viscosity increased while pasting temperature decreased with increase in fineness of the fractions. The results showed that the successive reduction dry milling of a single corn type could produce fractions with varied characteristics. [Copyright &y& Elsevier]

Published
2009
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