Topic: company:openai-research

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1. GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

   Paper • Dec 20, 2021 • arXiv • Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen

   Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guiding technique to trade off diversity for fidelity. We explore diffusion models...

2. Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

   Paper • Dec 14, 2023 • arXiv • Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever, Jeff Wu

   As AI models become increasingly capable, we will eventually face the challenge of superalignment: how can humans supervise AI systems that are much smarter than them? To study this empirically tod...

3. Consistency Models

   Paper • Mar 2, 2023 • arXiv • Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever

   Diffusion models have achieved significant success in image, audio, and video generation, but they depend on an iterative generation process that causes slow sampling and precludes real-time applic...

4. Sora: Video generation models as world simulators

   Paper • Feb 15, 2024 • OpenAI Technical Report • Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, Aditya Ramesh

   We explore the large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of highly variable durations, resolutio...

5. WebGPT: Browser-assisted question-answering with human feedback

   Paper • Dec 16, 2021 • arXiv • Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman

   We introduce a method for fine-tuning language models to interact with a text-based web browser to answer open-ended questions. This model, WebGPT, searches the web, navigates through links, and sy...

6. Learning Dexterous In-Hand Manipulation

   Paper • Jul 30, 2018 • arXiv • Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, Wojciech Zaremba

   We demonstrate that reinforcement learning algorithms can be used to learn highly dexterous, in-hand manipulation policies that successfully transfer to the real world. We train a policy to control...

7. Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

   Paper • Jan 6, 2022 • arXiv • Alethea Power, Yuri Burda, Harri Edwards, Igor Babuschkin, Vedant Misra

   We demonstrate a striking phenomenon in the training dynamics of neural networks on small algorithmic datasets: networks that initially severely overfit the training data can, after continued train...

8. Improved Denoising Diffusion Probabilistic Models

   Paper • Feb 18, 2021 • arXiv • Alex Nichol, Prafulla Dhariwal

   Denoising diffusion probabilistic models (DDPMs) have recently demonstrated high-quality image generation, but they suffer from notoriously slow sampling times and sub-optimal log-likelihoods. We p...

9. Dota 2 with Large Scale Deep Reinforcement Learning

   Paper • Dec 13, 2019 • arXiv • Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Ilya Sutskever, et al.

   We present OpenAI Five, a system of five neural networks that learned to play the highly complex, imperfect-information esports game Dota 2 entirely through self-play. Dota 2 involves long time hor...

10. Learning to summarize from human feedback

    Paper • Sep 2, 2020 • arXiv • Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul Christiano

    We show that it is possible to significantly improve the quality of text summaries generated by large language models by training them with reinforcement learning from human feedback. We collect a ...

11. Solving Rubik's Cube with a Robot Hand

    Paper • Oct 15, 2019 • arXiv • Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Peter Welinder, Lilian Weng, Wojciech Zaremba, Lei Zhang

    We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. We use reinforcement learning to train a policy to sol...

12. Evaluating Large Language Models Trained on Code

    Paper • Jul 7, 2021 • arXiv • Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Wojciech Zaremba, Ilya Sutskever, et al.

    We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copi...

13. Zero-Shot Text-to-Image Generation

    Paper • Feb 24, 2021 • arXiv • Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever

    Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. We describe a simple approach for this task based on a transformer that au...

14. Scaling Laws for Neural Language Models

    Paper • Jan 23, 2020 • arXiv • Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei

    We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, ...

15. Improving Language Understanding by Generative Pre-Training

    Paper • Jun 11, 2018 • OpenAI • Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever

    Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classification. Although large un...

16. Robust Speech Recognition via Large-Scale Weak Supervision

    Paper • Dec 6, 2022 • arXiv • Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever

    We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask su...

17. Hierarchical Text-Conditional Image Generation with CLIP Latents

    Paper • Apr 13, 2022 • arXiv • Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen

    Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a tw...

18. Language Models are Unsupervised Multitask Learners

    Paper • Feb 14, 2019 • OpenAI • Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever

    Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on task-specific data...

19. Proximal Policy Optimization Algorithms

    Paper • Jul 20, 2017 • arXiv • John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov

    We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a 'surrogate' objective...

20. Learning Transferable Visual Models From Natural Language Supervision

    Paper • Feb 26, 2021 • arXiv • Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever

    State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories, restricting their generality. We demonstrate that the simple pre-training task of pre...