PTA: From Pretrained Representations to Acting Agents -- Bridging Pretraining, Planning, and Test-Time Decision Making

Large-scale pretraining has become a dominant paradigm across machine learning, providing foundation models for language, vision, robotics, multimodal interaction, and decision-support systems. Yet as pretrained models are increasingly deployed in sequential decision-making settings, one central challenge remains: how can pretrained representations be learned, aligned with acting agents, and transformed into reliable, adaptive, and controllable behavior at test time? The PTA workshop—From Pretrained Representations to Acting Agents—will bring together researchers from representation learning, reinforcement learning, robotics, world modeling, planning, test-time scaling, and adaptive control to study this emerging post-pretraining decision interface. The workshop will focus on what makes representations actionable: how they encode structure useful for prediction, planning, control, adaptation, uncertainty estimation, and generalization, and how agents can reliably use such knowledge under distribution shift and changing task demands. It will examine both the learning of action-relevant abstractions and the mechanisms that convert pretrained knowledge into robust decisions during deployment. By emphasizing the shared problem of learning representations that are not merely predictive but actionable, PTA aims to clarify common principles, expose open challenges, identify benchmark needs, and accelerate progress toward agents that can reliably reuse, adapt, and deploy pretrained knowledge in dynamic environments.