243 citations · 270 across the 4 of their papers we have counts for
5 papers
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning
Gheorghe Comanici, Amelia Glaese, Anita Gergely +5
Hierarchical Reinforcement Learning (HRL) allows interactive agents to decompose complex problems into a hierarchy of sub-tasks. Higher-level tasks can invoke the solutions of lowe…
Red Teaming Language Models with Language Models
Ethan Perez, Saffron Huang, Francis Song +6
Language Models (LMs) often cannot be deployed because of their potential to harm users in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using…
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.…
Challenges in Detoxifying Language Models
Johannes Welbl, Amelia Glaese, Jonathan Uesato +7
Large language models (LM) generate remarkably fluent text and can be efficiently adapted across NLP tasks. Measuring and guaranteeing the quality of generated text in terms of saf…
AndroidEnv: A Reinforcement Learning Platform for Android
Daniel Toyama, Philippe Hamel, Anita Gergely +6
We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem. AndroidEnv allows RL agents to interact with a wide…