title: OpenAI o1 System Card
summary: 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.
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paper_authors.name: OpenAI
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paper_authors.name: Aaron Jaech
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paper_authors.name: Adam Kalai
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paper_authors.name: Adam Lerer
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paper_authors.name: Adam Richardson
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paper_authors.name: Ahmed El-Kishky
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paper_authors.name: Aiden Low
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paper_authors.name: Alec Helyar
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paper_authors.name: Aleksander Madry
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paper_authors.name: Alex Beutel
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paper_authors.name: Alex Carney
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paper_authors.name: Alex Iftimie
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paper_authors.name: Alex Karpenko
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paper_authors.name: Alex Tachard Passos
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paper_authors.name: Alexander Neitz
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paper_authors.name: Alexander Prokofiev
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paper_authors.name: Alexander Wei
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paper_authors.name: Allison Tam
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paper_authors.name: Ally Bennett
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paper_authors.name: Ananya Kumar
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paper_authors.name: Andre Saraiva
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paper_authors.name: Andrea Vallone
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paper_authors.name: Andrew Kondrich
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paper_authors.name: Andrey Mishchenko
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paper_authors.name: Andy Applebaum
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paper_authors.name: Angela Jiang
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paper_authors.name: Ashvin Nair
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paper_authors.name: Barret Zoph
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paper_authors.name: Behrooz Ghorbani
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paper_authors.name: Benjamin Sokolowsky
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paper_authors.name: Boaz Barak
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paper_authors.name: Bob McGrew
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paper_authors.name: Shengli Hu
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paper_authors.name: Shibani Santurkar
paper_authors._id: 676a5f1127c8341daf37c91a
paper_authors.name: Shraman Ray Chaudhuri
paper_authors._id: 676a5f1127c8341daf37c91b
paper_authors.name: Shuyuan Zhang
paper_authors._id: 676a5f1127c8341daf37c91c
paper_authors.name: Siyuan Fu
paper_authors._id: 676a5f1127c8341daf37c91d
paper_authors.name: Spencer Papay
paper_authors._id: 676a5f1127c8341daf37c91e
paper_authors.name: Steph Lin
paper_authors._id: 676a5f1127c8341daf37c91f
paper_authors.name: Suchir Balaji
paper_authors._id: 676a5f1127c8341daf37c920
paper_authors.name: Suvansh Sanjeev
paper_authors._id: 676a5f1127c8341daf37c921
paper_authors.name: Szymon Sidor
paper_authors._id: 676a5f1127c8341daf37c922
paper_authors.name: Tal Broda
paper_authors._id: 676a5f1127c8341daf37c923
paper_authors.name: Aidan Clark
paper_authors._id: 676a5f1127c8341daf37c924
paper_authors.name: Tao Wang
paper_authors._id: 676a5f1127c8341daf37c925
paper_authors.name: Taylor Gordon
paper_authors._id: 676a5f1127c8341daf37c926
paper_authors.name: Ted Sanders
paper_authors._id: 676a5f1127c8341daf37c927
paper_authors.name: Tejal Patwardhan
paper_authors._id: 676a5f1127c8341daf37c928
paper_authors.name: Thibault Sottiaux
paper_authors._id: 676a5f1127c8341daf37c929
paper_authors.name: Thomas Degry
paper_authors._id: 676a5f1127c8341daf37c92a
paper_authors.name: Thomas Dimson
paper_authors._id: 676a5f1127c8341daf37c92b
paper_authors.name: Tianhao Zheng
paper_authors._id: 676a5f1127c8341daf37c92c
paper_authors.name: Timur Garipov
paper_authors._id: 676a5f1127c8341daf37c92d
paper_authors.name: Tom Stasi
paper_authors._id: 676a5f1127c8341daf37c92e
paper_authors.name: Trapit Bansal
paper_authors._id: 676a5f1127c8341daf37c92f
paper_authors.name: Trevor Creech
paper_authors._id: 676a5f1127c8341daf37c930
paper_authors.name: Troy Peterson
paper_authors._id: 676a5f1127c8341daf37c931
paper_authors.name: Tyna Eloundou
paper_authors._id: 676a5f1127c8341daf37c932
paper_authors.name: Valerie Qi
paper_authors._id: 676a5f1127c8341daf37c933
paper_authors.name: Vineet Kosaraju
paper_authors._id: 676a5f1127c8341daf37c934
paper_authors.name: Vinnie Monaco
paper_authors._id: 676a5f1127c8341daf37c935
paper_authors.name: Vitchyr Pong
paper_authors._id: 676a5f1127c8341daf37c936
paper_authors.name: Vlad Fomenko
paper_authors._id: 676a5f1127c8341daf37c937
paper_authors.name: Weiyi Zheng
paper_authors._id: 676a5f1127c8341daf37c938
paper_authors.name: Wenda Zhou
paper_authors._id: 676a5f1127c8341daf37c939
paper_authors.name: Wes McCabe
paper_authors._id: 676a5f1127c8341daf37c93a
paper_authors.name: Wojciech Zaremba
paper_authors._id: 676a5f1127c8341daf37c93b
paper_authors.name: Yann Dubois
paper_authors._id: 676a5f1127c8341daf37c93c
paper_authors.name: Yinghai Lu
paper_authors._id: 676a5f1127c8341daf37c93d
paper_authors.name: Yining Chen
paper_authors._id: 676a5f1127c8341daf37c93e
paper_authors.name: Young Cha
paper_authors._id: 676a5f1127c8341daf37c93f
paper_authors.name: Yu Bai
paper_authors._id: 676a5f1127c8341daf37c940
paper_authors.name: Yuchen He
paper_authors._id: 676a5f1127c8341daf37c941
paper_authors.name: Yuchen Zhang
paper_authors._id: 676a5f1127c8341daf37c942
paper_authors.name: Yunyun Wang
paper_authors._id: 676a5f1127c8341daf37c943
paper_authors.name: Zheng Shao
paper_authors._id: 676a5f1127c8341daf37c944
paper_authors.name: Zhuohan Li
paper_title: OpenAI o1 System Card
paper_summary: 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.
paper_ai_keywords: reinforcement learning chain of thought reasoning capabilities safety policies deliberative alignment benchmarks illicit advice stereotyped responses jailbreaks robust alignment methods safety evaluations external red teaming Preparedness Framework evaluations