# stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation

arXiv: https://arxiv.org/abs/2605.21800 · HTML: https://mlsanity.com/paper/2605.21800/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Video-generation world models and simulators, one of 11 directions in World models.
- Builds on 50 of its 68 references that are in the map.
- First posted on arXiv 20 May 2026.

## Placed in

- World models / [Video-generation world models and simulators](https://mlsanity.com/wm/video-generation-world-models-and-simulators/)

## Builds on (most cited first, 30 of 50)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024)
- [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019)
- [TD-MPC2: Scalable, Robust World Models for Continuous Control](https://mlsanity.com/paper/2310.16828/) (2023)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Temporal Difference Learning for Model Predictive Control](https://mlsanity.com/paper/2203.04955/) (2022)
- [DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning](https://mlsanity.com/paper/2411.04983/) (2024)
- [Diffusion for World Modeling: Visual Details Matter in Atari](https://mlsanity.com/paper/2405.12399/) (2024)
- [LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels](https://mlsanity.com/paper/2603.19312/) (2026)
- [Navigation World Models](https://mlsanity.com/paper/2412.03572/) (2024)
- [LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics](https://mlsanity.com/paper/2511.08544/) (2025)
- [OGBench: Benchmarking Offline Goal-Conditioned RL](https://mlsanity.com/paper/2410.20092/) (2024)
- [Training Agents Inside of Scalable World Models](https://mlsanity.com/paper/2509.24527/) (2025)

## Built on by (most cited first, 20 of 20)

- [World Model Control by Trajectory Reachability Metrics](https://mlsanity.com/paper/2605.22164/) (2026)
- [LpWM: A Case for Sparse Representations in World Models](https://mlsanity.com/paper/2608.22764/) (2026)
- [FF-JEPA: Long-Horizon Planning in World Models with Latent Planners](https://mlsanity.com/paper/2606.09311/) (2026)
- [PhyLatent: Learning Dynamics-Relevant Representations for JEPA World Models](https://mlsanity.com/paper/2608.05720/) (2026)
- [IMWM: Intuition Models Complement World Models for Latent Planning](https://mlsanity.com/paper/2606.01626/) (2026)
- [Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models](https://mlsanity.com/paper/2607.23602/) (2026)
- [Reinforced Planning with Latent World Models](https://mlsanity.com/paper/2608.18669/) (2026)
- [Mind the Gap: Promises and Pitfalls of Hierarchical Planning in LeWorldModel](https://mlsanity.com/paper/2607.12547/) (2026)
- [Listening Forward: Next Patch Embedding Prediction Enables Scalable Audio Learners](https://mlsanity.com/paper/2608.19863/) (2026)
- [Beyond Task Success: Stage-Wise Reliability of World Model Planning under Sensing Degradation](https://mlsanity.com/paper/2609.07126/) (2026)
- [ATLAS: Aligned Transport of Latent Structure for Reliable World Model Planning](https://mlsanity.com/paper/2609.36333/) (2026)
- [Do-JEPA: From Masking to Intervention in Latent World Models](https://mlsanity.com/paper/2609.37378/) (2026)
- [CF-JEPA: Improving Robustness of JEPA World Models via Controllability Factorization](https://mlsanity.com/paper/2610.00727/) (2026)
- [Learning Commute-Time-Preserving World Models for Planning](https://mlsanity.com/paper/2610.01373/) (2026)
- [Keeping JEPA World Models Plannable When Little of the Frame Moves](https://mlsanity.com/paper/2610.03137/) (2026)
- [What Should World Models Forget? Stratified Retention for Continual Adaptation](https://mlsanity.com/paper/2610.03713/) (2026)
- [Flow Policies as Actions of Skill-Level World Models: Learned and Symbolic Abstractions for Long-Horizon Planning](https://mlsanity.com/paper/2610.04767/) (2026)
- [How Long, Not How Close: A Learned Temporal Metric for Planning in Latent World Models](https://mlsanity.com/paper/2610.04988/) (2026)
- [LeWAM: End-to-End World and Action Modeling with JEPAs](https://mlsanity.com/paper/2610.12818/) (2026)
- [LeHami: Symplectic Latent Dynamics for World Models](https://mlsanity.com/paper/2610.15267/) (2026)

## Abstract (arXiv)

World models are central to building agents that can reason, plan, and generalize beyond their training data. However, research on world models is currently fragmented, with disparate codebases, data pipelines, and evaluation protocols hindering reproducibility and fair comparison. Current practice is further limited by three key bottlenecks: fragile one-off codebases, slow video data loading, and the lack of standardized generalization benchmarks. We present stable-worldmodel (swm), an open-source platform for standardized and reproducible world modeling research and evaluation. It delivers (1) a high-performance Lance-based data layer with native support and conversion tools for MP4, HDF5, and LeRobot datasets, (2) clean, well-tested implementations of modern world model baselines and planning solvers, and (3) a broad suite of environments and tasks extended with controllable visual, geometric, and physical factors of variation for systematic in-silico evaluation of dynamics understanding, control performance, representation quality, and out-of-distribution generalization. By unifying the full pipeline under a single, scalable framework, textttswm dramatically reduces research overhead and accelerates trustworthy progress toward reliable world models.