Title: Latent Skill Models for Offline Reinforcement Learning Abstract: Offline reinforcement learning (RL) holds promise as a means to learn high-value policies from a static dataset, without the need for further environment interactions. However, a key challenge in offline RL lies in effectively stitching portions of suboptimal trajectories from the static dataset while avoiding extrapolation [...]