LeFlow: Generative Latent Flow Planning for World Models

Key points

LeFlow uses a rectified flow model on top of a frozen latent world model to generate reusable latent paths connecting the current state to a goal.
An inverse-dynamics decoder converts the generated latent transitions into action chunks, and autoregressive rollouts of the world model validate and select from a fixed number of candidates.
Across four goal-conditioned pixel-control benchmarks, success rates were consistently higher than with iterative action-space optimization, while planning time was reduced by a factor of roughly 10.
Its practical significance is that it extends a world model beyond a simple predictor into a fast planner reusable across different states and goals.
The current method is limited to short, fixed planning horizons. Over longer horizons, the generation problem grows and rollout errors from the frozen predictor accumulate.

External research summaries. These are not HDATF publications or measured product results.