Open-source Dreamer world-model implementation in JAX
https://github.com/next-state/open-dreamer
Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation
https://github.com/CLeARoboticsLab/simdist
A tiny, fully-reproducible Joint-Embedding Predictive Architecture world model
https://github.com/mandarwagh9/dvd-jepa
A Minimal and Elegant Framework & Tutorial for Real-Time Interactive World Models
https://github.com/shengshu-ai/minWM
Robotic World Model Lite
https://github.com/leggedrobotics/robotic_world_model_lite
Hierarchical Planning with Latent World Models
https://arxiv.org/abs/2604.03208
Unified Codebase for Advanced World Models
https://github.com/OpenDCAI/OpenWorldLib
stable-worldmodel - World model research made simple. From data collection to training and evaluation
https://github.com/galilai-group/stable-worldmodel
DreamDojo - A Generalist Robot World Model from Large-Scale Human Videos
https://dreamdojo-world.github.io
VideoWorld Series: Learning World Models from Unlabeled Videos
https://github.com/ByteDance-Seed/VideoWorld
Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments
https://arxiv.org/abs/2601.01075
HY-World 1.5: A Systematic Framework for Interactive World Modeling
https://github.com/Tencent-Hunyuan/HY-WorldPlay
PAN: A World Model for General, Interactable, and Long-Horizon World Simulation
https://arxiv.org/abs/2511.09057
Ctrl-World: A Controllable Generative World Model for Robot Manipualtion
https://github.com/Robert-gyj/Ctrl-World
Training Agents Inside of Scalable World Models
https://danijar.com/project/dreamer4/
TinyWorlds is a minimal autoregressive world model built on Google Deepmind's Genie Architecture
https://arxiv.org/pdf/2402.15391
Semantic World Models
https://weirdlabuw.github.io/swm/
GigaBrain-0: A World Model-Powered Vision-Language-Action Model
https://huggingface.co/papers/2510.19430
Code World Models for General Game Playing
https://arxiv.org/abs/2510.04542
Code World Model
https://github.com/facebookresearch/cwm
Symbolic World Models in Lean 4 for Reinforcement Learning
https://openreview.net/forum?id=CBFAMnax09¬eId=CBFAMnax09
Imagined Autocurricula
https://arxiv.org/abs/2509.13341
Uncovering Untapped Potential in Sample-Efficient World Model Agents
https://arxiv.org/abs/2502.11537v3
Reinforcing World Model Reasoning for Multi-Turn VLM Agents
https://vagen-ai.github.io/