9 papers
Recurrent Structural Policy Gradient for Partially Observable Mean Field Games
Clarisse Wibault, Johannes Forkel, Sebastian Towers +9
Mean Field Games (MFGs) provide a principled framework for modelling interactions in large population systems. However, algorithmic progress has been limited since model-free metho…
Asking the Right Questions: Improving Reasoning with Generated Stepping Stones
Hengyuan Hu, Tingchen Fu, Minqi Jiang +3
Recent years have witnessed tremendous progress in enabling LLMs to solve complex reasoning tasks such as math and coding. As we start to apply LLMs to harder tasks that they may n…
Learning to Reason at the Frontier of Learnability
Thomas Foster, Anya Sims, Johannes Forkel +2
Reinforcement learning is now widely adopted as the final stage of large language model training, especially for reasoning-style tasks such as maths problems. Typically, models att…
Infusion: Shaping Model Behavior by Editing Training Data via Influence Functions
J Rosser, Robert Kirk, Edward Grefenstette +2
Influence functions are commonly used to attribute model behavior to training documents. We explore the reverse: crafting training data that induces model behavior. Our framework,…
Improving Regret Approximation for Unsupervised Dynamic Environment Generation
Harry Mead, Bruno Lacerda, Jakob Foerster +1
Unsupervised Environment Design (UED) seeks to automatically generate training curricula for reinforcement learning (RL) agents, with the goal of improving generalisation and zero-…
Déjà Q: Open-Ended Evolution of Diverse, Learnable and Verifiable Problems
Willem Röpke, Samuel Coward, Andrei Lupu +3
Recent advances in reasoning models have yielded impressive results in mathematics and coding. However, most approaches rely on static datasets, which have been suggested to encour…