7 citations · 12 across the 13 of their papers we have counts for
6 papers · 1 filter
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps
Benjamin Ellis, Matthew T. Jackson, Andrei Lupu +4
In reinforcement learning (RL), it is common to apply techniques used broadly in machine learning such as neural network function approximators and momentum-based optimizers. Howev…
CURATe: Benchmarking Personalised Alignment of Conversational AI Assistants
Lize Alberts, Benjamin Ellis, Andrei Lupu +1
We introduce a multi-turn benchmark for evaluating personalised alignment in LLM-based AI assistants, focusing on their ability to handle user-provided safety-critical contexts. Ou…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
Behaviour Distillation
Andrei Lupu, Chris Lu, Jarek Liesen +2
Dataset distillation aims to condense large datasets into a small number of synthetic examples that can be used as drop-in replacements when training new models. It has application…
Discovering Minimal Reinforcement Learning Environments
Jarek Liesen, Chris Lu, Andrei Lupu +3
Reinforcement learning (RL) agents are commonly trained and evaluated in the same environment. In contrast, humans often train in a specialized environment before being evaluated,…
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu +9
As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to adversarial attacks is of paramou…