  In tasks aiming for long-term returns, planning becomes essential. We study
generative modeling for planning with datasets repurposed from offline
reinforcement learning. Specifically, we identify temporal consistency in the
absence of step-wise rewards as one key technical challenge. We introduce the
Latent Plan Transformer (LPT), a novel model that leverages a latent space to
connect a Transformer-based trajectory generator and the final return. LPT can
be learned with maximum likelihood estimation on trajectory-return pairs. In
learning, posterior sampling of the latent variable naturally integrates
sub-trajectories to form a consistent abstraction despite the finite context.
At test time, the latent variable is inferred from an expected return before
policy execution, realizing the idea of planning as inference. Our experiments
demonstrate that LPT can discover improved decisions from suboptimal
trajectories, achieving competitive performance across several benchmarks,
including Gym-Mujoco, Franka Kitchen, Maze2D, and Connect Four. It exhibits
capabilities in nuanced credit assignments, trajectory stitching, and
adaptation to environmental contingencies. These results validate that latent
variable inference can be a strong alternative to step-wise reward prompting.
