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11/28/2022
Fine-tuning language models to find agreement among humans with diverse preferences
Recent work in large language modeling (LLMs) has used fine-tuning to al...
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11/03/2022
Fine-Tuning Language Models via Epistemic Neural Networks
Large language models are now part of a powerful new paradigm in machine...
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09/28/2022
Improving alignment of dialogue agents via targeted human judgements
We present Sparrow, an information-seeking dialogue agent trained to be ...
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03/21/2022
Teaching language models to support answers with verified quotes
Recent large language models often answer factual questions correctly. B...
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02/07/2022
Red Teaming Language Models with Language Models
Language Models (LMs) often cannot be deployed because of their potentia...
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06/01/2020
Acme: A Research Framework for Distributed Reinforcement Learning
Deep reinforcement learning has led to many recent-and groundbreaking-ad...
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04/23/2020
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning
Standard planners for sequential decision making (including Monte Carlo ...
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08/09/2019
Behaviour Suite for Reinforcement Learning
This paper introduces the Behaviour Suite for Reinforcement Learning, or...
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06/12/2019
When to use parametric models in reinforcement learning?
We examine the question of when and how parametric models are most usefu...
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02/01/2019
TF-Replicator: Distributed Machine Learning for Researchers
We describe TF-Replicator, a framework for distributed machine learning ...
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06/08/2018
Randomized Prior Functions for Deep Reinforcement Learning
Dealing with uncertainty is essential for efficient reinforcement learni...
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05/30/2017
Universal Reinforcement Learning Algorithms: Survey and Experiments
Many state-of-the-art reinforcement learning (RL) algorithms typically a...
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05/22/2017