---
title: "Self-Supervised World Models | Category | world-models.io"
canonical: "https://world-models.io/en/categories/self-supervised-world-models/"
description: "World models that learn environment dynamics through self-supervised prediction without explicit labels. Review Self-Supervised World Models: evidence."
author: "Bernard Grenat"
generated: "2026-10-30"
---

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The Knowledge Hub for AI World Models

# Self-Supervised World Models

Self-supervised world models learn representations of environment dynamics without explicit labels or reward signals, leveraging prediction in abstract representation space.

robotics

model-based-rl

simulation

embodied-ai

## What is a world model?

A world model is an AI system that learns a predictive representation of how an environment changes over time. It can use observations, actions, or language to estimate future states and possible outcomes before an agent acts. World models support planning in model-based reinforcement learning, robotics, embodied AI, autonomous driving, and interactive simulation. This site catalogs these systems with model records, research papers, benchmark context, and source links. Start with the [world models database](https://world-models.io/en/world-models-database/), compare systems on the [leaderboard](https://world-models.io/en/leaderboard/), or read the [Performance Index methodology](https://world-models.io/en/methodology/performance-index/).

## What Is Self-Supervised World Models?

Editorial definition preserved directly in static HTML.

Self-supervised world models learn representations of environment dynamics without explicit labels or reward signals. They leverage prediction in abstract representation space, predicting future states, video frames, or embeddings, to build internal models of how the world works.

World models that learn environment dynamics through self-supervised prediction without explicit labels.

## Category Snapshot

Static category overview generated from local editorial data.

| Attribute | Value |
| --- | --- |
| Category | Self-Supervised World Models |
| Description | World models that learn environment dynamics through self-supervised prediction without explicit labels. |
| Definition | Self-supervised world models learn representations of environment dynamics without explicit labels or reward signals. They leverage prediction in abstract representation space, predicting future states, video frames, or embeddings, to build internal models of how the world works. |
| Related Models | 3 |
| Related Research | 1 |
| Related Guides | 0 |

## Why This Category Matters

Additional editorial context preserved directly in static HTML.

### Why Self-Supervised Learning Changes the Story

Self-supervised world models reduce dependence on explicit rewards and labels, making them especially relevant for large-scale pretraining from raw video or interaction data.

### How to Evaluate a Self-Supervised World Model

What matters is whether the representation transfers well, preserves predictive structure, and supports later planning or control rather than only whether the pretraining objective looks elegant.

## Related Models

| Model | Lab | Category | Year |
| --- | --- | --- | --- |
| [V-JEPA](https://world-models.io/en/models/v-jepa/) | Meta | Self-Supervised World Model | 2024 |
| [World Models (Ha & Schmidhuber)](https://world-models.io/en/models/ha-world-model/) | Google Brain / IDSIA | Model-Based RL | 2018 |
| [RSSM](https://world-models.io/en/models/rssm/) | Google | Latent Dynamics | 2019 |

## Related Research

| Topic | Summary |
| --- | --- |
| [Self-Supervised World Models](https://world-models.io/en/research/self-supervised-world-models/) | How self-supervised world models learn environment dynamics without rewards, from JEPA and V-JEPA to predictive latent representations. |

## Frequently Asked Questions

FAQ answers rendered directly into static HTML for extractable responses.

### How do self-supervised world models learn?

They learn by predicting future observations or latent representations without explicit reward labels, often through contrastive, masked, or JEPA-style objectives.

## Quick Answer

Short extractable summary preserved directly in static HTML.

- Self-Supervised World Models is a category used on world-models.io to organize related models, research topics, and guides around a common technical theme.
- Use this page to understand the category definition first, then move into the linked models, research, and guides.

## Editorial Trust Signals

Editorial provenance and refresh policy preserved directly in static HTML.

**Published by** world-models.io editorial board.

**Lead editor** Bernard Grenat.

This category page maintains a stable editorial definition and connects it to related models, research topics, guides, and source-backed context.

Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-09-27.

Pages are refreshed when a new paper, benchmark, release, architecture update, or stronger primary source materially changes the answer a reader or AI system should retrieve.

Each page links back to relevant primary sources and keeps a stable canonical URL so readers can verify claims, trace context, and reference the most up-to-date version. [See the editorial policy.](https://world-models.io/en/editorial-policy/)

Primary sources only

Last reviewed date visible

Methodology documented

Source links included

Correction channel published

Review date visible

## Representative Sources

Representative external references connected to this category through related models and research topics.

- [V-JEPA paper](https://arxiv.org/abs/2404.08471) Meta primary research source
- [World Models (Ha & Schmidhuber) paper](https://arxiv.org/abs/1803.10122) Google Brain / IDSIA primary research source
- [World Models (Ha & Schmidhuber) official page](https://worldmodels.github.io) Google Brain / IDSIA official page
- [RSSM paper](https://arxiv.org/abs/1811.04551) Google primary research source