2 citations · 4 across the 9 of their papers we have counts for
11 papers
Evolution Strategies at the Hyperscale
Bidipta Sarkar, Mattie Fellows, Juan Agustin Duque +17
Evolution Strategies (ES) is a class of powerful black-box optimisation methods that are highly parallelisable and can handle non-differentiable and noisy objectives. However, naïv…
Imagined Autocurricula
Ahmet H. Güzel, Matthew Thomas Jackson, Jarek Luca Liesen +4
Training agents to act in embodied environments typically requires vast training data or access to accurate simulation, neither of which exists for many cases in the real world. In…
Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents
Davide Paglieri, Bartłomiej Cupiał, Jonathan Cook +6
Training large language models (LLMs) to reason via reinforcement learning (RL) significantly improves their problem-solving capabilities. In agentic settings, existing methods lik…
Intent Factored Generation: Unleashing the Diversity in Your Language Model
Eltayeb Ahmed, Uljad Berdica, Martha Elliott +2
Obtaining multiple meaningfully diverse, high quality samples from Large Language Models for a fixed prompt remains an open challenge. Current methods for increasing diversity ofte…
StochasTok: Improving Fine-Grained Subword Understanding in LLMs
Anya Sims, Thom Foster, Klara Kaleb +5
Subword-level understanding is integral to numerous tasks, including understanding multi-digit numbers, spelling mistakes, abbreviations, rhyming, and wordplay. Despite this, curre…
A Clean Slate for Offline Reinforcement Learning
Matthew Thomas Jackson, Uljad Berdica, Jarek Liesen +2
Progress in offline reinforcement learning (RL) has been impeded by ambiguous problem definitions and entangled algorithmic designs, resulting in inconsistent implementations, insu…
AgentBreeder: Mitigating the AI Safety Risks of Multi-Agent Scaffolds via Self-Improvement
J Rosser, Jakob Foerster
Scaffolding Large Language Models (LLMs) into multi-agent systems often improves performance on complex tasks, but the safety impact of such scaffolds has not been thoroughly explo…
The Complexity Dynamics of Grokking
Branton DeMoss, Silvia Sapora, Jakob Foerster +2
We demonstrate the existence of a complexity phase transition in neural networks by studying the grokking phenomenon, where networks suddenly transition from memorization to genera…
BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
Davide Paglieri, Bartłomiej Cupiał, Samuel Coward +10
Large Language Models (LLMs) and Vision Language Models (VLMs) possess extensive knowledge and exhibit promising reasoning abilities, however, they still struggle to perform well i…
Meta-Learning Objectives for Preference Optimization
Carlo Alfano, Silvia Sapora, Jakob Nicolaus Foerster +2
Evaluating preference optimization (PO) algorithms on LLM alignment is a challenging task that presents prohibitive costs, noise, and several variables like model size and hyper-pa…
TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation
Jonathan Cook, Tim Rocktäschel, Jakob Foerster +2
Given the widespread adoption and usage of Large Language Models (LLMs), it is crucial to have flexible and interpretable evaluations of their instruction-following ability. Prefer…