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cs.LG2026
Optimistic Task Inference for Behavior Foundation Models
Thomas Rupf, Marco Bagatella, Marin Vlastelica +1
Behavior Foundation Models (BFMs) are capable of retrieving high-performing policy for any reward function specified directly at test-time, commonly referred to as zero-shot reinfo…
cs.LG2026
Soft Forward-Backward Representations for Zero-shot Reinforcement Learning with General Utilities
Marco Bagatella, Thomas Rupf, Georg Martius +1
Recent advancements in zero-shot reinforcement learning (RL) have facilitated the extraction of diverse behaviors from unlabeled, offline data sources. In particular, forward-backw…
cs.LG2025
Zero-Shot Offline Imitation Learning via Optimal Transport
Thomas Rupf, Marco Bagatella, Nico Gürtler +2
Zero-shot imitation learning algorithms hold the promise of reproducing unseen behavior from as little as a single demonstration at test time. Existing practical approaches view th…