Jan Leike | DeepAI

DeepAI AI Chat

Featured Co-authors

Sergey Levine
366 publications
Pushmeet Kohli
91 publications
Marcus Hutter
80 publications
Stuart Russell
61 publications
Anca D. Dragan
61 publications
Tor Lattimore
56 publications
Ilya Sutskever
53 publications
Felix Hill
43 publications
Chong Zhang
43 publications
Edward Grefenstette
42 publications
Shane Legg
40 publications

∙ 05/31/2023

Let's Verify Step by Step

In recent years, large language models have greatly improved in their ab...

1 Hunter Lightman, et al. ∙

∙ 06/12/2022

Self-critiquing models for assisting human evaluators

We fine-tune large language models to write natural language critiques (...

8 William Saunders, et al. ∙

∙ 03/04/2022

Training language models to follow instructions with human feedback

Making language models bigger does not inherently make them better at fo...

1 Long Ouyang, et al. ∙

∙ 01/20/2022

Safe Deep RL in 3D Environments using Human Feedback

Agents should avoid unsafe behaviour during both training and deployment...

5 Matthew Rahtz, et al. ∙

∙ 09/22/2021

Recursively Summarizing Books with Human Feedback

A major challenge for scaling machine learning is training models to per...

0 Jeff Wu, et al. ∙

∙ 07/07/2021

Evaluating Large Language Models Trained on Code

We introduce Codex, a GPT language model fine-tuned on publicly availabl...

6 Mark Chen, et al. ∙

∙ 05/30/2021

Institutionalising Ethics in AI through Broader Impact Requirements

Turning principles into practice is one of the most pressing challenges ...

0 Carina Prunkl, et al. ∙

∙ 11/13/2020

Active Reinforcement Learning: Observing Rewards at a Cost

Active reinforcement learning (ARL) is a variant on reinforcement learni...

0 David Krueger, et al. ∙

∙ 09/19/2020

Hidden Incentives for Auto-Induced Distributional Shift

Decisions made by machine learning systems have increasing influence on ...

9 David Krueger, et al. ∙

∙ 06/24/2020

Quantifying Differences in Reward Functions

For many tasks, the reward function is too complex to be specified proce...

23 Adam Gleave, et al. ∙

∙ 04/28/2020

Pitfalls of learning a reward function online

In some agent designs like inverse reinforcement learning an agent needs...

6 Stuart Armstrong, et al. ∙

∙ 12/05/2019

Learning Human Objectives by Evaluating Hypothetical Behavior

We seek to align agent behavior with a user's objectives in a reinforcem...

20 Siddharth Reddy, et al. ∙

∙ 12/14/2018

Scaling shared model governance via model splitting

Currently the only techniques for sharing governance of a deep learning ...

6 Miljan Martic, et al. ∙

∙ 11/19/2018

Scalable agent alignment via reward modeling: a research direction

One obstacle to applying reinforcement learning algorithms to real-world...

20 Jan Leike, et al. ∙

∙ 11/15/2018

Reward learning from human preferences and demonstrations in Atari

To solve complex real-world problems with reinforcement learning, we can...

6 Borja Ibarz, et al. ∙

∙ 06/05/2018

Learning to Follow Language Instructions with Adversarial Reward Induction

Recent work has shown that deep reinforcement-learning agents can learn ...

14 Dzmitry Bahdanau, et al. ∙

∙ 11/27/2017

AI Safety Gridworlds

We present a suite of reinforcement learning environments illustrating v...

0 Jan Leike, et al. ∙

∙ 06/12/2017

Deep reinforcement learning from human preferences

For sophisticated reinforcement learning (RL) systems to interact useful...

0 Paul Christiano, et al. ∙

∙ 05/30/2017

Universal Reinforcement Learning Algorithms: Survey and Experiments

Many state-of-the-art reinforcement learning (RL) algorithms typically a...

0 John Aslanides, et al. ∙

∙ 11/28/2016

Nonparametric General Reinforcement Learning

Reinforcement learning (RL) problems are often phrased in terms of Marko...

0 Jan Leike, et al. ∙

∙ 09/16/2016

A Formal Solution to the Grain of Truth Problem

A Bayesian agent acting in a multi-agent environment learns to predict t...

0 Jan Leike, et al. ∙

∙ 09/16/2016

Exploration Potential

We introduce exploration potential, a quantity that measures how much a ...

0 Jan Leike, et al. ∙

∙ 04/12/2016

Loss Bounds and Time Complexity for Speed Priors

This paper establishes for the first time the predictive performance of ...

0 Daniel Filan, et al. ∙

∙ 02/25/2016

Thompson Sampling is Asymptotically Optimal in General Environments

We discuss a variant of Thompson sampling for nonparametric reinforcemen...

0 Jan Leike, et al. ∙

∙ 10/19/2015

On the Computability of AIXI

How could we solve the machine learning and the artificial intelligence ...

0 Jan Leike, et al. ∙

∙ 10/16/2015

Bad Universal Priors and Notions of Optimality

A big open question of algorithmic information theory is the choice of t...

0 Jan Leike, et al. ∙

∙ 05/18/2015

A Definition of Happiness for Reinforcement Learning Agents

What is happiness for reinforcement learning agents? We seek a formal de...

0 Mayank Daswani, et al. ∙