59 citations · 80 across the 7 of their papers we have counts for
3 papers · 1 filter
Diffusion-Pretrained Dense and Contextual Embeddings
Sedigheh Eslami, Maksim Gaiduk, Markus Krimmel +3
In this report, we introduce pplx-embed, a family of multilingual embedding models that employ multi-stage contrastive learning on a diffusion-pretrained language model backbone fo…
Gymnasium: A Standard Interface for Reinforcement Learning Environments
Mark Towers, Ariel Kwiatkowski, Jordan Terry +13
Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research…
Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planning
Jan Achterhold, Markus Krimmel, Joerg Stueckler
Problems which require both long-horizon planning and continuous control capabilities pose significant challenges to existing reinforcement learning agents. In this paper we introd…