39 citations · 159 across the 23 of their papers we have counts for
5 papers · 1 filter
Learning from negative feedback, or positive feedback or both
Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari +9
Existing preference optimization methods often assume scenarios where paired preference feedback (preferred/positive vs. dis-preferred/negative examples) is available. This require…
Game On: Towards Language Models as RL Experimenters
Jingwei Zhang, Thomas Lampe, Abbas Abdolmaleki +2
We propose an agent architecture that automates parts of the common reinforcement learning experiment workflow, to enable automated mastery of control domains for embodied agents.…
Imitating Language via Scalable Inverse Reinforcement Learning
Markus Wulfmeier, Michael Bloesch, Nino Vieillard +13
The majority of language model training builds on imitation learning. It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning…
Offline Actor-Critic Reinforcement Learning Scales to Large Models
Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang +9
We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning. We find that of…
GATS: Gather-Attend-Scatter
Konrad Zolna, Serkan Cabi, Yutian Chen +5
As the AI community increasingly adopts large-scale models, it is crucial to develop general and flexible tools to integrate them. We introduce Gather-Attend-Scatter (GATS), a nove…