[2303.08774] GPT-4 Technical Report - arXiv.org
Mar 15, 2023 · View a PDF of the paper titled GPT-4 Technical Report, by OpenAI and 279 other authors View PDF HTML (experimental) Abstract: We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Research | OpenAI
We build our generative models using a technology called deep learning, which leverages large amounts of data to train an AI system to perform a task. Text. Our text models are advanced language processing tools that can generate, classify, and summarize text with high levels of coherence and accuracy.
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
Sep 27, 2024 · This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, …
Training language models to follow instructions with human …
Mar 4, 2022 · In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 ...
Research - OpenAI
Explore the research we're conducting to stay at the forefront of AI development and deployment.
In this paper, we explore a semi-supervised approach for language understanding tasks using a combination of unsupervised pre-training and supervised fine-tuning. Our goal is to learn a universal
OpenAI Abstract We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated
In this white paper, we lay out several practices that different actors can implement to mitigate the risk of harm from agentic AI systems, which could serve as building blocks for a set of agreed baseline best practices. We also highlight the many areas where operationalizing these practices
GPT-4 - OpenAI
GPT-4 is the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 was trained on Microsoft Azure AI supercomputers. Azure’s AI-optimized infrastructure also allows us to deliver GPT-4 to users around the world.
Key Papers in Deep RL — Spinning Up documentation - OpenAI
What follows is a list of papers in deep RL that are worth reading. This is far from comprehensive, but should provide a useful starting point for someone looking to do research in the field. 1. Model-Free RL. 2. Exploration. 3. Transfer and Multitask RL. 4. Hierarchy. 5. Memory. 6. Model-Based RL. 7. Meta-RL. 8. Scaling RL. 9. RL in the Real World