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Learning to play Minecraft with Video PreTraining
We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data. With fine-tuning, our model can learn to craft diamond tools, a task that usually takes proficient humans over 20 minutes (24,000 actions). Our model uses the native human interface of keypresses and mouse movements, making it quite general, and represents a step towards general computer-using agents.
AI-written critiques help humans notice flaws
We trained “critique-writing” models to describe flaws in summaries. Human evaluators find flaws in summaries much more often when shown our model’s critiques. Larger models are better at self-critiquing, with scale improving critique-writing more than summary-writing. This shows promise for using AI systems to assist human supervision of AI systems on difficult tasks.
Techniques for training large neural networks
Large neural networks are at the core of many recent advances in AI, but training them is a difficult engineering and research challenge which requires orchestrating a cluster of GPUs to perform a single synchronized calculation.
Best practices for deploying language models
Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.
Teaching models to express their uncertainty in words
Hierarchical text-conditional image generation with CLIP latents
Economic impacts research at OpenAI
Call for expressions of interest to study the economic impacts of large language models.
Lessons learned on language model safety and misuse
We describe our latest thinking in the hope of helping other AI developers address safety and misuse of deployed models.
A research agenda for assessing the economic impacts of code generation models
Solving (some) formal math olympiad problems
We built a neural theorem prover for Lean that learned to solve a variety of challenging high-school olympiad problems, including problems from the AMC12 and AIME competitions, as well as two problems adapted from the IMO.
Aligning language models to follow instructions
Text and code embeddings by contrastive pre-training
WebGPT: Improving the factual accuracy of language models through web browsing
We’ve fine-tuned GPT-3 to more accurately answer open-ended questions using a text-based web browser.
Solving math word problems
We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset, while our system scored 55% on those same problems.
Summarizing books with human feedback
Scaling human oversight of AI systems for tasks that are difficult to evaluate.
TruthfulQA: Measuring how models mimic human falsehoods
Evaluating large language models trained on code
Improving language model behavior by training on a curated dataset
Our latest research finds we can improve language model behavior with respect to specific behavioral values by fine-tuning on a small, curated dataset.
OpenAI Scholars 2021: Final projects
We’re proud to announce that the 2021 class of OpenAI Scholars has completed our six-month mentorship program and have produced an open-source research project with stipends and support from OpenAI.
Multimodal neurons in artificial neural networks
We’ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIP’s accuracy in classifying surprising visual renditions of concepts, and is also an important step toward understanding the associations and biases that CLIP and similar models learn.