PTA: From Pretrained Representations to Acting Agents

Sequential decision-making is undergoing a transition where agents increasingly rely on pretrained representations learned from large-scale interaction data, videos, and multimodal corpora, yet a central challenge remains: how can pretrained representations be aligned with acting agents and transformed into effective behavior?

The PTA workshop will focus on this emerging interface between pretraining and control. Pretrained models can encode rich semantic, temporal, causal, or structural information. Still, acting agents must transform that knowledge into reliable behavior, including choosing actions over time, adapting to new goals, reasoning under uncertainty, recovering from mistakes, and remaining robust under distribution shift.

We invite submissions that investigate how pretrained knowledge can become actionable for sequential decision-making. Beyond proposing new methods, we particularly encourage work that studies the principles, limitations, evaluation methodologies, and trade-offs underlying different approaches. By bringing together researchers from reinforcement learning, representation learning, robotics, planning, and foundation models, the workshop aims to facilitate discussion toward a deeper understanding of when and why pretrained representations enable effective decision making.