Latent Dynamics · 2026-10-23
An Introduction to World Models for Generalist Agents
Zhihui Zhu
AI-assisted summary based on the paper. Consult the linked source for methods and limitations. Benchmark numbers appear after editor verification.
TL;DR
The authors provide an introductory, unified perspective on “world models” for generalist agents using a POMDP framework. They connect world modeling to classical control, dynamical systems, signal processing, and reinforcement learning, and explain how world modeling and policy learning can be related through conditional prediction (potentially without requiring separate models).
Why it matters
World models are used to describe many different modeling ideas (predictive dynamics, learned simulators, generative video, world-action models). The paper clarifies what “world model” means in this context, how it relates to POMDP decision-making and control, and how conditional prediction can unify world modeling with policy learning for generalist agents.
Method
- Formulate generalist-agent sequential decision-making using POMDPs with environments e and tasks g, interaction histories h_t, and policies π(a_t|h_t,g).
- Propose a broad definition of a world model as a learned representation capturing aspects of an environment’s structure, state, or dynamics to support understanding, prediction, or decision-making.
- Relate world modeling and policy learning through a unified probabilistic view of conditional distributions; discuss conditional prediction and world-action models integrating world prediction with action generation.
Limitation
The authors do not attempt an exhaustive survey of the rapidly growing literature.
Abstract (from arXiv)
World models have recently emerged as a promising direction toward developing generalist agents capable of understanding, predicting, and interacting with diverse environments. However, despite growing interest, the concept of a world model remains loosely defined, encompassing approaches ranging from predictive dynamics models and learned simulators to models that jointly generate future observations and actions. This introductory paper aims to clarify the concept of world models and provide a unified perspective on their role in developing generalist agents. Starting from the partially observable Markov decision process (POMDP) framework, we connect world modeling to classical control, dynamical systems, signal processing, and reinforcement learning. We introduce different approaches to world modeling, emphasizing the knowledge they capture, their learning objectives, and how they support decision-making. We further explore the relationship between world modeling and policy learning, highlighting how these capabilities can be unified through conditional prediction without requiring separate models. Rather than offering an exhaustive survey of existing methods, this paper focuses on fundamental concepts, modeling principles, and connections, with the goal of providing an accessible and unified introduction to world models for generalist agents.
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Publication record
Bibliographic evidence describes where and when a record appeared. It does not validate scientific claims.
First public release: 2026-10-22 · Indexed by Pub-AI: 2026-10-23 UTC
An Introduction to World Models for Generalist Agents
Preprint posted: 2026-10-22 · Original record ↗
- arxiv
- 2610.30789
Explicit source versions (1)
- v1 · 2026-10-22
- Preprint available
Source date: 2026-10-22 · First detected: 2026-10-25 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · atom:published (official metadata)
Abstract · arxiv
World models have recently emerged as a promising direction toward developing generalist agents capable of understanding, predicting, and interacting with diverse environments. However, despite growing interest, the concept of a world model remains loosely defined, encompassing approaches ranging from predictive dynamics models and learned simulators to models that jointly generate future observations and actions. This introductory paper aims to clarify the concept of world models and provide a unified perspective on their role in developing generalist agents. Starting from the partially observable Markov decision process (POMDP) framework, we connect world modeling to classical control, dynamical systems, signal processing, and reinforcement learning. We introduce different approaches to world modeling, emphasizing the knowledge they capture, their learning objectives, and how they support decision-making. We further explore the relationship between world modeling and policy learning, highlighting how these capabilities can be unified through conditional prediction without requiring separate models. Rather than offering an exhaustive survey of existing methods, this paper focuses on fundamental concepts, modeling principles, and connections, with the goal of providing an accessible and unified introduction to world models for generalist agents.