Offline data, pretrained policies and foundation models can make online RL more efficient. Their usefulness, however, changes across deployments and during training. This position paper argues for diagnosing those changes and adapting how the learner uses its priors. It connects this perspective to model post-training and embodied intelligence. Read the arXiv summary.
NeurIPS 2026 position track.