114 citations · 154 across the 12 of their papers we have counts for
7 papers · 1 filter
Stein Variational Evolution Strategies
Cornelius V. Braun, Robert T. Lange, Marc Toussaint
Stein Variational Gradient Descent (SVGD) is a highly efficient method to sample from an unnormalized probability distribution. However, the SVGD update relies on gradients of the…
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
Chris Lu, Cong Lu, Robert Tjarko Lange +3
One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models h…
NAVIX: Scaling MiniGrid Environments with JAX
Eduardo Pignatelli, Jarek Liesen, Robert Tjarko Lange +3
As Deep Reinforcement Learning (Deep RL) research moves towards solving large-scale worlds, efficient environment simulations become crucial for rapid experimentation. However, mos…
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,…
Discovering Preference Optimization Algorithms with and for Large Language Models
Chris Lu, Samuel Holt, Claudio Fanconi +4
Offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. Typically, preference optimization is approached as…
Discovering Temporally-Aware Reinforcement Learning Algorithms
Matthew Thomas Jackson, Chris Lu, Louis Kirsch +3
Recent advancements in meta-learning have enabled the automatic discovery of novel reinforcement learning algorithms parameterized by surrogate objective functions. To improve upon…