# Beyond the Next Step: Variable-Length Latent World Models for Long-Horizon Planning

arXiv: https://arxiv.org/abs/2606.21775 · HTML: https://mlsanity.com/paper/2606.21775/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Latent predictive world models, one of 11 directions in World models.
- Builds on 17 of its 21 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Placed in

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

## Builds on (most cited first, 17 of 17)

- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion](https://mlsanity.com/paper/2506.08009/) (2025)
- [Revisiting Feature Prediction for Learning Visual Representations from Video](https://mlsanity.com/paper/2404.08471/) (2024)
- [TD-MPC2: Scalable, Robust World Models for Continuous Control](https://mlsanity.com/paper/2310.16828/) (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)
- [OGBench: Benchmarking Offline Goal-Conditioned RL](https://mlsanity.com/paper/2410.20092/) (2024)
- [Understanding World or Predicting Future? A Comprehensive Survey of World Models](https://mlsanity.com/paper/2411.14499/) (2024)
- [HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels](https://mlsanity.com/paper/2507.21809/) (2025)
- [Learning to Generate Long-term Future via Hierarchical Prediction](https://mlsanity.com/paper/1704.05831/) (2017)
- [LongCat-Video Technical Report](https://mlsanity.com/paper/2510.22200/) (2025)
- [Hierarchical Planning with Latent World Models](https://mlsanity.com/paper/2604.03208/) (2026)
- [VA-RED2: Video Adaptive Redundancy Reduction](https://mlsanity.com/paper/2102.07887/) (2021)
- [ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model](https://mlsanity.com/paper/2603.22281/) (2026)

## Built on by (most cited first, 8 of 8)

- [SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning](https://mlsanity.com/paper/2607.17973/) (2026)
- [DWM: Separating World Effects from Actions in Latent World Models](https://mlsanity.com/paper/2607.18715/) (2026)
- [ActSWM: Action-Sensitive World Models for Long-Horizon Planning in Open-World Games](https://mlsanity.com/paper/2607.26712/) (2026)
- [Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models](https://mlsanity.com/paper/2607.23602/) (2026)
- [Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not](https://mlsanity.com/paper/2607.21645/) (2026)
- [FlexiWorld: Learning and Planning via Flexible Action Chunks Across Multiple Time Scales](https://mlsanity.com/paper/2609.35138/) (2026)
- [Beyond a single latent space: a dual-latent world model for long-horizon planning](https://mlsanity.com/paper/2609.37644/) (2026)
- [Long-Horizon Textual World Modeling through Structured Reasoning](https://mlsanity.com/paper/2610.06637/) (2026)

## Abstract (arXiv)

Recently, world models have emerged as a promising paradigm for building intelligent agents by learning predictive models that estimate future environment states conditioned on observations and actions. In particular, JEPA-style latent world models provide an efficient alternative to pixel space prediction by learning action-conditioned dynamics in compact representation spaces. However, existing latent world models typically rely on one-step prediction and must be recursively rolled out for long-horizon planning, which leads to compounding errors and a mismatch between training objectives and downstream planning tasks. To address this limitation, we propose Variable-length Latent World Models (VLWMs), a framework that learns to predict future latent states conditioned on action sequences of variable lengths. Instead of training only on one-step transitions, VLWMs directly model temporally extended dynamics, allowing the same predictor to evaluate action plans over different horizons. We further introduce a curriculum training strategy that progressively expands the action horizon, stabilizing optimization from short-range dynamics to long-range prediction. At test time, we design planning methods tailored to VLWMs to better exploit their variable-length predictive capabilities. Experiments on long-horizon control tasks show that VLWMs significantly improve latent space world models, achieving 13% average improvement over the state-of-the-art LeWM across different datasets, with especially large gains on tasks requiring extended planning. These results suggest that VLWM provides a simple yet effective paradigm for improving long-horizon prediction and planning in latent world models.