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09/20/2023
Chain-of-Verification Reduces Hallucination in Large Language Models
Generation of plausible yet incorrect factual information, termed halluc...
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07/19/2023
Challenges and Applications of Large Language Models
Large Language Models (LLMs) went from non-existent to ubiquitous in the...
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07/03/2023
Improving Language Plasticity via Pretraining with Active Forgetting
Pretrained language models (PLMs) are today the primary model for natura...
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06/08/2023
On the Importance of Exploration for Generalization in Reinforcement Learning
Existing approaches for improving generalization in deep reinforcement l...
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06/05/2023
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs
Exploration in environments which differ across episodes has received in...
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06/02/2023
Hyperparameters in Reinforcement Learning and How To Tune Them
In order to improve reproducibility, deep reinforcement learning (RL) ha...
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03/06/2023
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning
Open-ended learning methods that automatically generate a curriculum of ...
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02/15/2023
Augmented Language Models: a Survey
This survey reviews works in which language models (LMs) are augmented w...
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02/09/2023
Toolformer: Language Models Can Teach Themselves to Use Tools
Language models (LMs) exhibit remarkable abilities to solve new tasks fr...
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11/18/2022
Building a Subspace of Policies for Scalable Continual Learning
The ability to continuously acquire new knowledge and skills is crucial ...
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11/01/2022
Dungeons and Data: A Large-Scale NetHack Dataset
Recent breakthroughs in the development of agents to solve challenging s...
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10/11/2022
Exploration via Elliptical Episodic Bonuses
In recent years, a number of reinforcement learning (RL) methods have be...
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03/22/2022
Insights From the NeurIPS 2021 NetHack Challenge
In this report, we summarize the takeaways from the first NeurIPS 2021 N...
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02/17/2022
Improving Intrinsic Exploration with Language Abstractions
Reinforcement learning (RL) agents are particularly hard to train when r...
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07/27/2021
Open-Ended Learning Leads to Generally Capable Agents
In this work we create agents that can perform well beyond a single, ind...
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02/20/2021
Decoupling Value and Policy for Generalization in Reinforcement Learning
Standard deep reinforcement learning algorithms use a shared representat...
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07/06/2020
Fast Adaptation via Policy-Dynamics Value Functions
Standard RL algorithms assume fixed environment dynamics and require a s...
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06/24/2020
The NetHack Learning Environment
Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand wit...
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06/23/2020
Automatic Data Augmentation for Generalization in Deep Reinforcement Learning
Deep reinforcement learning (RL) agents often fail to generalize to unse...
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06/22/2020
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
A key challenge for reinforcement learning (RL) consists of learning in ...
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02/27/2020
RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments
Exploration in sparse reward environments remains one of the key challen...
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07/18/2018
Backplay: "Man muss immer umkehren"
A long-standing problem in model free reinforcement learning (RL) is tha...
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02/26/2018
