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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 Self-Supervised Learning?

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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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AttributeValue
TermSelf-Supervised Learning
CategoryTraining
DefinitionA 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 Models2
Related Research1

Related Models

ModelLabCategory
V-JEPAMetaSelf-Supervised World Model
World Models (Ha & Schmidhuber)Google Brain / IDSIAModel-Based RL

Related Research

TopicSummary
Self-Supervised World ModelsHow self-supervised world models learn environment dynamics without rewards, from JEPA and V-JEPA to predictive latent representations.

Related Terms

TermCategoryDefinition
JEPA (Joint Embedding Predictive Architecture)ArchitectureA 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 LearningTrainingA 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

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  • Self-Supervised Learning is a glossary concept used across world-models.io to clarify language, methods, and architectural ideas in the field.
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Published by world-models.io editorial board.

Lead editor Bernard Grenat.

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