Improving Language Understanding by Generative Pre …

Generative Pretraining from Pixels - OpenAI

Generative Pretraining from Pixels (Radford et al.,2019) formulation of the transformer de-coder block, which acts on an input tensor hlas follows: nl= layer norm(hl) al= hl+multihead attention(nl) hl+1 = al+mlp(layer norm(al)) In particular, layer norms precede both the attention and

  Form, Generative, Pixel, Generative pretraining from pixels, Pretraining

Language Models are Unsupervised Multitask Learners

Language Models are Unsupervised Multitask Learners Alec Radford * 1Jeffrey Wu Rewon Child David Luan 1Dario Amodei ** Ilya Sutskever ** 1 Abstract Natural language processing tasks, such as ques-tion answering, machine translation, reading com-

Benchmarking Safe Exploration in Deep Reinforcement …

range of prior work on safe reinforcement learning, we propose to standardize constrained RL as the main formalism for safe exploration. Second, we present the Safety Gym benchmark suite, a new slate of high-dimensional continuous control environments for measuring research progress on constrained RL. Finally, we

  Control, Learning, Reinforcement, Reinforcement learning

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

human feedback. To make human evaluation of factual accuracy easier, models ... or negative ways of talking about people’s religion, skin color, ability, or gender [3]. Often, people say bad words when they are experiencing strong emotions, …

  Feedback, Negative

Jukebox: A Generative Model for Music - OpenAI

frequencies perceptible to humans. As an example, a four-minute-long audio segment will have an input length of ˘10 million, where each position can have 16 bits of information. In comparison, a high-resolution RGB image with 1024 1024 pixels has an input length of ˘3 million, and each position has 24 bits of information. This makes learning

  Frequencies, Jukebox

Dota 2 with Large Scale Deep Reinforcement Learning

Dota 2 with Large Scale Deep Reinforcement Learning OpenAI, ChristopherBerner,GregBrockman,BrookeChan,VickiCheung, Przemysław“Psyho"Dębiak,ChristyDennison ...

  Learning, Deep, Reinforcement, Otda, Deep reinforcement learning

Formal Mathematics Statement Curriculum Learning

more automation (such as more domain-specific statements generator or even informal to formal machine translation). 1.1. miniF2F benchmark In this work, we target the miniF2F (Zheng et al.,2021) benchmark, which consists of 244 validation and 244 test formalized statements of mathematical problems from var-ious competitions.

  Automation

Training language models to follow instructions with human ...

language models with human intent. 1 Introduction Large language models (LMs) can be “prompted” to perform a range of natural language process-ing (NLP) tasks, given some examples of the task as input. However, these models often express unintended behaviors such as making up facts, generating biased or toxic text, or simply not following

  Language, Model, Language model

Learning Transferable Visual Models From Natural …

of learning from natural language supervision. We study the scalability of CLIP by training a series of eight models spanning almost 2 orders of magnitude of compute and ob-serve that transfer performance is a smoothly predictable function of …

  Form, Language, Model, Learning, Visual, Natural, Transferable, Learning transferable visual models from natural

Language Models are Unsupervised Multitask Learners

Language Models are Unsupervised Multitask Learners to infer and perform many different tasks on examples with this type of format. Language modeling is also able to, in principle, learn the tasks ofMcCann et al.(2018) without the need for explicit supervision of which symbols are the outputs to be pre-dicted.

  Language

arXiv:1810.04805v2 [cs.CL] 24 May 2019

pre-training for language representations. Un-likeRadford et al.(2018), which uses unidirec-tional language models for pre-training, BERT uses masked language models to enable pre-trained deep bidirectional representations. This is also in contrast toPeters et al.(2018a), which uses a shallow concatenation of independently

  Language