Search Results for author:
Found 16 papers, 9 papers with code
Fine-tuning language models to find agreement among humans with diverse preferences
Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user.
Fine-Tuning Language Models via Epistemic Neural Networks
Language models often pre-train on large unsupervised text corpora, then fine-tune on additional task-specific data.
Improving alignment of dialogue agents via targeted human judgements
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines.
Teaching language models to support answers with verified quotes
We measure the performance of GopherCite by conducting human evaluation of answers to questions in a subset of the NaturalQuestions and ELI5 datasets.
Red Teaming Language Models with Language Models
In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases ("red teaming") using another LM.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
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College Mathematics
on BIG-bench
(using extra training data)
Divide-and-Conquer Monte Carlo Tree Search
are constrained by an implicit sequential planning assumption: The order in which a plan is constructed is the same in which it is executed.
Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning
are constrained by an implicit sequential planning assumption: The order in which a plan is constructed is the same in which it is executed.
Behaviour Suite for Reinforcement Learning
bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives.
When to use parametric models in reinforcement learning?
We examine the question of when and how parametric models are most useful in reinforcement learning.
TF-Replicator: Distributed Machine Learning for Researchers
We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow.
Randomized Prior Functions for Deep Reinforcement Learning
Dealing with uncertainty is essential for efficient reinforcement learning.
Universal Reinforcement Learning Algorithms: Survey and Experiments
Many state-of-the-art reinforcement learning (RL) algorithms typically assume that the environment is an ergodic Markov Decision Process (MDP).
AIXIjs: A Software Demo for General Reinforcement Learning
The universal Bayesian agent AIXI (Hutter, 2005) is a model of a maximally intelligent agent, and plays a central role in the sub-field of general reinforcement learning (GRL).
Generalised Discount Functions applied to a Monte-Carlo AImu Implementation
We have added to the GRL simulation platform AIXIjs the functionality to assign an agent arbitrary discount functions, and an environment which can be used to determine the effect of discounting on an agent's policy.
