# FF-JEPA: Long-Horizon Planning in World Models with Latent Planners

arXiv: https://arxiv.org/abs/2606.09311 · HTML: https://mlsanity.com/paper/2606.09311/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Latent predictive world models, one of 11 directions in World models.
- Builds on 12 of its 14 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Placed in

- World models / [Latent predictive world models](https://mlsanity.com/wm/latent-predictive-world-models/)

## Builds on (most cited first, 12 of 12)

- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning](https://mlsanity.com/paper/2411.04983/) (2024)
- [LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels](https://mlsanity.com/paper/2603.19312/) (2026)
- [Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation](https://mlsanity.com/paper/1909.05829/) (2019)
- [Latent Diffusion Planning for Imitation Learning](https://mlsanity.com/paper/2504.16925/) (2025)
- [stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation](https://mlsanity.com/paper/2605.21800/) (2026)
- [Disentangled Robot Learning via Separate Forward and Inverse Dynamics Pretraining](https://mlsanity.com/paper/2604.16391/) (2026)
- [stable-pretraining-v1: Foundation Model Research Made Simple](https://mlsanity.com/paper/2511.19484/) (2025)
- [Sparse Imagination for Efficient Visual World Model Planning](https://mlsanity.com/paper/2506.01392/) (2025)
- [When Does Predictive Inverse Dynamics Outperform Behavior Cloning?](https://mlsanity.com/paper/2601.21718/) (2026)
- [Grounding Generated Videos in Feasible Plans via World Models](https://mlsanity.com/paper/2602.01960/) (2026)

## Built on by (most cited first, 6 of 6)

- [SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning](https://mlsanity.com/paper/2607.17973/) (2026)
- [A Generalization Theory for JEPA-Based World Models](https://mlsanity.com/paper/2606.27014/) (2026)
- [QQWorld: Quantile-Quantile Matching for World Model Regularization](https://mlsanity.com/paper/2607.28415/) (2026)
- [Conformal Orbit-Valid Trust Horizons for Equivariant World Models](https://mlsanity.com/paper/2606.24946/) (2026)
- [Metro-WM: Long-Horizon Latent Planning with Realisable Sub-Goals](https://mlsanity.com/paper/2609.31868/) (2026)
- [Beyond a single latent space: a dual-latent world model for long-horizon planning](https://mlsanity.com/paper/2609.37644/) (2026)

## Abstract (arXiv)

Joint Embedding Predictive Architectures (JEPAs) have shown promising world modeling capabilities, enabling planning in latent space by optimizing action trajectories using methods like the Cross-Entropy Method (CEM). These methods are, however, too computationally expensive and ineffective for long-horizon planning. Furthermore, these methods typically require an explicit image of the goal state, which is not always possible in real-world tasks. In this work, we tackle these limitations by proposing Forward-Forward-JEPA (FF-JEPA), a hierarchical approach leveraging two forward dynamics models. Alongside a standard action-conditioned forward model, we introduce an action-free latent planner that predicts the next subgoal given the current state. This approach removes the need for goal images and enables long-horizon planning by decomposing complex trajectories into a sequence of tractable, short-term optimization problems. Preliminary results on PushT demonstrate that FF-JEPA successfully overcomes flat world models' long-horizon collapse, highlighting this approach as a promising direction for goal-free planning.