---
title: "Sample Efficiency | Glossary | world-models.io"
canonical: "https://world-models.io/en/glossary/sample-efficiency/"
description: "Definition of sample efficiency in AI: the ability to learn effectively from a small number of real-world interactions."
author: "Bernard Grenat"
generated: "2026-10-30"
---

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The Knowledge Hub for AI World Models

# Sample Efficiency

The ability of an algorithm to learn effectively from a small number of real-world interactions, enhanced by world models that supplement real data with imagined experience.

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 Sample Efficiency?

Editorial definition preserved directly in static HTML.

The ability of an algorithm to learn effectively from a small number of real-world interactions. Model-based RL agents achieve high sample efficiency by supplementing real data with imagined experience from their world model.

Sample Efficiency is a glossary concept in the concepts layer of the world models knowledge base.

## Term Snapshot

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

| Attribute | Value |
| --- | --- |
| Term | Sample Efficiency |
| Category | Concepts |
| Definition | The ability of an algorithm to learn effectively from a small number of real-world interactions. Model-based RL agents achieve high sample efficiency by supplementing real data with imagined experience from their world model. |
| Related Models | 3 |
| Related Research | 1 |

## Related Models

| Model | Lab | Category |
| --- | --- | --- |
| [DreamerV3](https://world-models.io/en/models/dreamer-v3/) | Google DeepMind | Model-Based RL |
| [MuZero](https://world-models.io/en/models/muzero/) | Google DeepMind | Model-Based RL |
| [TD-MPC2](https://world-models.io/en/models/td-mpc2/) | MIT / Meta | Model-Based RL |

## Related Research

| Topic | Summary |
| --- | --- |
| [Model-Based Reinforcement Learning](https://world-models.io/en/research/model-based-rl/) | What model-based reinforcement learning is, how world models enable imagination-based planning, and why Dreamer, MuZero, PlaNet, and TD-MPC2 matter. |

## Related Terms

| Term | Category | Definition |
| --- | --- | --- |
| [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. |
| [Model-Based Reinforcement Learning](https://world-models.io/en/glossary/model-based-rl-term/) | Paradigms | An RL approach where the agent learns a predictive model of the environment (a world model) and uses it to simulate outcomes, plan ahead, and learn from imagined experience. Contrasted with model-free RL which learns directly from interaction. |

## Quick Answer

Short extractable summary preserved directly in static HTML.

- Sample Efficiency 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/)

Primary sources only

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## Reference Sources

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

- [DreamerV3 paper](https://arxiv.org/abs/2301.04104) Google DeepMind primary research source
- [MuZero paper](https://arxiv.org/abs/1911.08265) Google DeepMind primary research source
- [TD-MPC2 paper](https://arxiv.org/abs/2310.16828) MIT / Meta primary research source
- [TD-MPC2 official page](https://tdmpc2.com) MIT / Meta official page