6 papers
Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents
Davide Paglieri, BartÅomiej CupiaÅ, 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…
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…
Synthetic Data is Sufficient for Zero-Shot Visual Generalization from Offline Data
Ahmet H. Güzel, Ilija Bogunovic, Jack Parker-Holder
Offline reinforcement learning (RL) offers a promising framework for training agents using pre-collected datasets without the need for further environment interaction. However, pol…
BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
Davide Paglieri, BartÅomiej CupiaÅ, 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…
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…
Multi-Agent Diagnostics for Robustness via Illuminated Diversity
Mikayel Samvelyan, Davide Paglieri, Minqi Jiang +2
In the rapidly advancing field of multi-agent systems, ensuring robustness in unfamiliar and adversarial settings is crucial. Notwithstanding their outstanding performance in famil…