---
title: "Self-Supervised Learning | Glossary | world-models.io"
canonical: "https://world-models.io/en/glossary/self-supervised-learning/"
description: "Definition of self-supervised learning: a paradigm where models generate their own training signal from unlabeled data."
author: "Bernard Grenat"
generated: "2026-10-30"
---

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# Self-Supervised Learning

A learning paradigm where the model generates its own training signal from unlabeled data, typically by predicting parts of the input from other parts.

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 Learning?

Editorial definition preserved directly in static HTML.

A learning paradigm where the model generates its own training signal from unlabeled data, typically by predicting parts of the input from other parts. Self-supervised world models learn environment dynamics without explicit reward signals.

Self-Supervised Learning is a glossary concept in the training layer of the world models knowledge base.

## Term Snapshot

Static glossary definition snapshot for crawlers and no-JS readers.

| Attribute | Value |
| --- | --- |
| Term | Self-Supervised Learning |
| Category | Training |
| Definition | A learning paradigm where the model generates its own training signal from unlabeled data, typically by predicting parts of the input from other parts. Self-supervised world models learn environment dynamics without explicit reward signals. |
| Related Models | 2 |
| Related Research | 1 |

## Related Models

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

## 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. |

## Related Terms

| Term | Category | Definition |
| --- | --- | --- |
| [JEPA (Joint Embedding Predictive Architecture)](https://world-models.io/en/glossary/jepa/) | Architecture | A framework proposed by Yann LeCun where models learn by predicting representations of future states in abstract embedding space, rather than reconstructing raw pixels. V-JEPA demonstrates this approach for video understanding and physical dynamics learning. |
| [Imagination-Based Learning](https://world-models.io/en/glossary/imagination-based-learning/) | Training | A training paradigm where an agent uses its world model to generate synthetic (imagined) trajectories, then learns policies from these imagined experiences rather than costly real-world interaction. This dramatically improves sample efficiency. |

## Quick Answer

Short extractable summary preserved directly in static HTML.

- Self-Supervised Learning is a glossary concept used across world-models.io to clarify language, methods, and architectural ideas in the field.
- Use this page to get the definition quickly, then continue into related models, research topics, and adjacent terms for context.

## 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 glossary page publishes stable definitions linked to related models, research topics, and primary-source 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/)

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Methodology documented

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## Reference Sources

Primary sources related to this term, surfaced directly in static HTML.

- [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
- [Self-Supervised World Models reference](https://openreview.net/pdf?id=BZ5a1r-kVsf) LeCun, 2022. A Path Towards Autonomous Machine Intelligence.