World Model Learning

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50 papers in the last four weeks, up 257% on the four weeks before. 0.5% of all new papers.

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Probing the Impact of Scale on Data-Efficient, Generalist Transformer World Models for Atari
May 9, 2026Jooyeon KimWorld Model Learning Transformer
Graduate School of Artificial Intelligence UNIST
GEM: Generating LiDAR World Model via Deformable Mamba
May 8, 2026Yang Wu, Zhaojiang Liu, Qiang Meng +5Mamba World Model Learning
SJTU · FDU · NTU +1
Sword: Style-Robust World Models as Simulators via Dynamic Latent Bootstrapping for VLA Policy Post-Training
May 8, 2026Jiaxuan Gao, Yongjian Guo, Zhong Guan +5World Model Learning World Models
Tianjin University · Tsinghua University · Swinburne University of Technology +1
Three-in-One World Model: Energy-Based Consistency, Prediction, and Counterfactual Inference for Marketing Intervention
May 8, 2026Junichiro NiimiCausal Effect Estimation World Model Learning
1Meijo University
Learning Visual Feature-Based World Models via Residual Latent Action
May 8, 2026Xinyu Zhang, Zhengtong Xu, Yutian Tao +3Latent Action Learning World Model Learning
Rutgers University · Purdue University · University of Wisconsin-Madison
AGWM: Affordance-Grounded World Models for Environments with Compositional Prerequisites
May 7, 2026Qinshi Zhang, Weipeng Deng, Zhihan Jiang +4World Model Learning Interactive World Models
University of California, San Diego · University of Hong Kong · Columbia University +2
Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models
May 7, 2026Nilaksh, Saurav Jha, Artem Zholus +1World Model Learning World Models for Robotics
Chandar Research Lab · Mila – Quebec AI Institute · Polytechnique Montréal +1
Earth-o1: A Grid-free Observation-native Atmospheric World Model
May 7, 2026Junchao Gong, Kaiyi Xu, Wangxu Wei +22World Model Learning Weather Forecasting
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. · School of Information Science and Technology, University of Science and Technology of China, Anhui, China. · State Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. +5
HaM-World: Soft-Hamiltonian World Models with Selective Memory for Planning
May 7, 2026Haoyun Tang, Haodong Cui, Keyao Xu +2World Model Learning Latent Dynamics Modeling
1Xi’an Jiaotong University · 2Huazhong University of Science and Technology · 3Nankai University +1
Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout
May 6, 2026Haozhuang Chi, Daosheng Qiu, Hao Su +4World Model Learning Causal World Models
Nanyang Technological University, Singapore · Hubei University, Wuhan, China · Osaka University, Osaka, Japan
Learning to Theorize the World from Observation
May 5, 2026Doojin Baek, Gyubin Lee, Junyeob Baek +2Computational Cognitive Modeling World Model Learning
KAIST
HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation
Apr 30, 2026Xin Zhou, Dingkang Liang, Xiwu Chen +4World Model Learning 3D Scene Generation
Huazhong University of Science and Technology · Mach Drive · University of Hong Kong
World Model for Robot Learning: A Comprehensive Survey
Apr 30, 2026Bohan Hou, Gen Li, Jindou Jia +15World Model Learning World Models for Robotics
Nanyang Technological University · University of California, Berkeley · Stanford University +6
Graph World Models: Concepts, Taxonomy, and Future Directions
Apr 30, 2026Jiawei Liu, Senqiao Yang, Mingjun Wang +2World Model Learning World Models
The Chinese University of Hong Kong, Hong Kong, China · Tsinghua University, Beijing, China
Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models
Apr 28, 2026Pengcheng Fang, Hongli Chen, Xiaohao CaiWorld Model Learning Action-Conditioned World Models
The University of Southampton · The University of Queensland
Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
Apr 24, 2026Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin +47World Model Learning Action-Conditioned World Models
Mask World Model: Predicting What Matters for Robust Robot Policy Learning
Apr 21, 2026Yunfan Lou, Xiaowei Chi, Xiaojie Zhang +9Robot Policy Learning Video Diffusion Models
National University of Singapore, Singapore · Beijing Academy of Artificial Intelligence, Beijing, China · The Hong Kong University of Science and Technology, Hong Kong, China +3
Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training
Apr 20, 2026Vin Bhaskara, Haicheng WangIntrinsic Reward Methods Reinforcement Learning
Department of Computer Science, University of Toronto, Toronto, Canada.
Infrastructure-Centric World Models: Bridging Temporal Depth and Spatial Breadth for Roadside Perception
Apr 19, 2026Siyuan Meng, Chengbo AiWorld Model Learning World Models
Department of Civil and Environmental Engineering, University of Massachusetts Amherst
Active World-Model with 4D-informed Retrieval for Exploration and Awareness
Apr 17, 2026Elaheh Vaezpour, Amirhosein Javadi, Tara JavidiActive Perception World Model Learning
KavAI · UCSD
Human Cognition in Machines: A Unified Perspective of World Models
Apr 17, 2026Timothy Rupprecht, Pu Zhao, Amir Taherin +20Computational Cognitive Modeling World Model Learning
Northeastern University, Boston, MA, USA · EmbodyX Inc., San Mateo, CA, USA · Tulane University, New Orleans, LA, USA +2
The Global Neural World Model: Spatially Grounded Discrete Topologies for Action-Conditioned Planning
Apr 17, 2026Noureddine KermicheWorld Model Learning World Model-Based Planning
Western Digital Corporation, Irvine, CA, USA
Learning Ad Hoc Network Dynamics via Graph-Structured World Models
Apr 16, 2026Can Karacelebi, Yusuf Talha Sahin, Elif Surer +1World Model Learning World Models
Department of Computer Engineering, Middle East Technical University, Ankara, Turkiye · Türk Telekom, Ankara, Turkiye · Graduate School of Informatics, Middle East Technical University, Ankara, Turkiye
Beyond State Consistency: Behavior Consistency in Text-Based World Models
Apr 15, 2026Youling Huang, Guanqiao Chen, Junchi Yao +8World Models World Model Learning
Dalian University of Technology · Microsoft · MBZUAI +1
Zero-shot World Models Are Developmentally Efficient Learners
Apr 11, 2026Khai Loong Aw, Klemen Kotar, Wanhee Lee +6Zero-Shot Learning Computational Cognitive Modeling
Stanford University
ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving
Apr 3, 2026Zihao Sheng, Xin Ye, Jingru Luo +2World Model Learning VLMs for Autonomous Driving
Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI) · University of Wisconsin–Madison
Language-Conditioned World Modeling for Visual Navigation
Mar 23, 2026Yifei Dong, Fengyi Wu, Yilong Dai +8World Model Learning Vision-Language Navigation
University of Washington · National University of Singapore · Clemson University +2
RAE-NWM: Navigation World Model in Dense Visual Representation Space
Mar 10, 2026Mingkun Zhang, Wangtian Shen, Fan Zhang +3Visual Navigation World Model Learning
Department of Precision Instrument, Tsinghua University, Beijing, China · University of Rochester, Rochester, NY, USA · Beijing Information Science and Technology University, Beijing, China
MetaOthello: A Controlled Study of Multiple World Models in Transformers
Feb 26, 2026Aviral Chawla, Galen Hall, Juniper LovatoTransformer Interpretability World Model Learning
Vermont Complex Systems Institute, University of Vermont, Burlington, VT · University of Michigan, Ann Arbor, MI
Neuro-Symbolic Synergy for World Modeling
Feb 11, 2026Hongyu Zhao, Siyu Zhou, Haolin Yang +2LLM World Models World Model Learning
Department of Computer Science University of Maryland College Park, MD 20742, USA · Faculty of Engineering and IT Australian AI Institute University of Technology Sydney Sydney, Australia · Department of Statistics University of Chicago Chicago, IL 60637, USA +2

May 9, 2026, cs.LGJ/K move · Enter open · S save

Probing the Impact of Scale on Data-Efficient, Generalist Transformer World Models for Atari

Jooyeon Kim

Graduate School of Artificial Intelligence UNIST

World Model Learning Transformer World Models Data-Efficient Learning

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Developing generalist systems that retain human-like data efficiency is a central challenge. While world models (WMs) offer a promising path, existing research often conflates architectural mechanisms with the independent impact of model \emph{scale}. In this work, we use a minimalist transformer world model to analyze scaling behaviors on the Atari 100k benchmark, using fixed offline datasets derived from a presupposed expert policy. Our results reveal that environments fundamentally fall into distinct scaling regimes, even when constrained by identical offline data budgets and model capacities. For individual tasks, some environments naturally allow models to pass the interpolation threshold, yielding monotonic improvements in the overparameterized regime, while others remain trapped in the classical regime, where larger world models degrade fidelity. In the unified setting, i.e., a single transformer trained on a suite of 26 Atari environments, we uncover that joint training stabilizes scaling dynamics, ensuring monotonic gains across all environments, regardless of their distinct inherent scaling regimes. Finally, we demonstrate that improved fidelity translates directly to downstream control, with policies learned entirely within the simulated dynamics achieving a median expert-random-normalized score of 0.770. Our findings suggest that future progress lies as much in precise scaling strategies as in architectural innovation.

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