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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.
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| 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 |
| Model | Lab | Category |
|---|---|---|
| V-JEPA | Meta | Self-Supervised World Model |
| World Models (Ha & Schmidhuber) | Google Brain / IDSIA | Model-Based RL |
| Topic | Summary |
|---|---|
| Self-Supervised World Models | How self-supervised world models learn environment dynamics without rewards, from JEPA and V-JEPA to predictive latent representations. |
| Term | Category | Definition |
|---|---|---|
| JEPA (Joint Embedding Predictive Architecture) | 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 | 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. |
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Published by world-models.io editorial board.
Lead editor Bernard Grenat.
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