---
title: "LWM | Large World Model | world-models.io"
canonical: "https://world-models.io/en/models/lwm/"
description: "LWM is UC Berkeley's Large World Model processing 1M+ tokens of video and text for long-context world understanding."
author: "Tyler D."
generated: "2026-10-30"
---

New: the Timeline is live. Track world model releases, papers, and benchmark updates in real time.

The Knowledge Hub for AI World Models

# LWM

LWM (Large World Model) is UC Berkeley's foundation model that processes over 1 million tokens of interleaved video and text, enabling long-horizon world understanding.

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/).

## Key Attributes

Static HTML snapshot of the model record for crawlers and no-JS readers.

| Attribute | Value |
| --- | --- |
| Model | Large World Model (LWM) |
| Lab / Organization | UC Berkeley |
| Category | Foundation World Model |
| Subtype | Long-Context Multimodal Model |
| World Model Type | Million-length video-language world model |
| Primary Domain | Video Understanding |
| Architecture | LLaMA-based transformer with RingAttention for million-token context |
| Modality | Visual (Video) + Language |
| Training Method | Progressive context extension on interleaved video-text data |
| Status | active |
| Year | 2024 |
| Performance Index | 55/100 (medium confidence, v1.1) |

## About Large World Model (LWM)

Main editorial body preserved directly in static HTML.

The Large World Model (LWM) extends the context length of multimodal transformers to over one million tokens, enabling processing of long videos interleaved with text. Built on the LLaMA architecture with RingAttention for efficient long-context training, LWM demonstrates that scaling sequence length (not just model size) unlocks emergent world understanding capabilities including long-horizon video comprehension, temporal reasoning, and cross-modal physical dynamics prediction.

Large World Model (LWM) is a million-length video-language world model developed by UC Berkeley in 2024 for video understanding.

## Editorial Snapshot

Short extractable facts for answer engines and no-JS readers.

| Signal | Value |
| --- | --- |
| Definition | Large World Model (LWM) is a million-length video-language world model developed by UC Berkeley in 2024 for video understanding. |
| Short Description | A foundation model trained on 1M+ interleaved video and language tokens for long-horizon world understanding. |
| Benchmark Rows | 1 |
| FAQ Entries | 2 |
| Related Models | 4 |
| Related Guides | 0 |
| Related Research Topics | 0 |
| Last Updated | 2026-04-07 |

## Notable Features

Key capabilities associated with this model.

- • 1M+ token context length
- • RingAttention for memory-efficient training
- • Interleaved video-text understanding
- • Emergent temporal reasoning

## Use Cases

Representative applications attached to this model record.

Long video understanding

Temporal event reasoning

Cross-modal question answering

Video summarization

## Strengths and Limitations

Balanced assessment surfaced in static HTML.

### Strengths

- • Unprecedented context length for video
- • Unified video-language architecture
- • Emergent world understanding
- • Open-source weights

### Limitations

- • High compute requirements
- • Limited action/planning capabilities
- • Primarily passive understanding, not generation

## Benchmarks

Published benchmark evidence attached to this model record.

| Benchmark | Metric | Result | Source |
| --- | --- | --- | --- |
| Long Video QA | Accuracy | State-of-the-art | [Source](https://arxiv.org/abs/2402.08855) |

## References and Citations

Primary references preserved in static HTML for citation extraction.

| Reference | Link |
| --- | --- |
| Liu et al., 2024. World Model on Million-Length Video And Language With RingAttention. arXiv:2402.08855 | [Open source](https://arxiv.org/abs/2402.08855) |

## Related Models

Nearby models linked from the current editorial record.

| Model | Category | World Model Type | Index v1.1 |
| --- | --- | --- | --- |
| [V-JEPA](https://world-models.io/en/models/v-jepa/) | Self-Supervised World Model | Self-supervised visual world model | 70/100 |
| [Sora](https://world-models.io/en/models/sora/) | Generative World Model | Text-to-video world simulator | 63/100 |
| [NVIDIA Cosmos](https://world-models.io/en/models/nvidia-cosmos/) | Foundation World Model | Video world foundation model | 87/100 |
| [AMI World Model](https://world-models.io/en/models/ami-world-model/) | Foundation World Model | Announced multimodal world model initiative | N/A |

## Direct Comparisons

Side-by-side comparisons already connected to this model.

| Comparison | Matchup | Summary |
| --- | --- | --- |
| [LWM vs V-JEPA](https://world-models.io/en/compare/lwm-vs-v-jepa/) | Large World Model (LWM) vs V-JEPA | Two approaches to learning world understanding from video. LWM uses autoregressive prediction over million-length sequences, while V-JEPA predicts abstract latent representations without pixel reconstruction. |

## Frequently Asked Questions

FAQ answers rendered directly into static HTML for extractable responses.

### Why is context length important for world models?

Longer context allows models to track persistent objects, understand cause-and-effect over time, and reason about physical dynamics across extended video sequences, essential capabilities for world understanding.

### How does LWM achieve 1M+ tokens?

LWM uses RingAttention, a technique that distributes attention computation across multiple devices, enabling linear scaling of context length with available hardware.

## Quick Answer

Short extractable summary preserved directly in static HTML.

- Large World Model (LWM) is a million-length video-language world model developed by UC Berkeley in 2024 for video understanding.
- Use this page when you need a fast read on how Large World Model (LWM) fits into the foundation world model landscape, then validate the details in the benchmarks, citations, and related pages.
- A key strength surfaced in the editorial record is unprecedented context length for video.

## Editorial Trust Signals

Editorial provenance and refresh policy preserved directly in static HTML.

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

**Lead editor** Tyler D. - Technical editor, methodology and benchmark analysis.

This model page synthesizes primary papers, official model pages, benchmark evidence, and related world-models.io context into a reference resource.

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: 2026-04-07.

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

## External Sources

Primary model and lab sources embedded in static HTML.

- [Large World Model (LWM) paper](https://arxiv.org/abs/2402.08855) UC Berkeley primary research source
- [Large World Model (LWM) official page](https://largeworldmodel.github.io/) UC Berkeley official page
- [UC Berkeley official website](https://bair.berkeley.edu) Related lab

## References

1. [[1] Liu et al., 2024. World Model on Million-Length Video And Language With RingAttention. arXiv:2402.08855](https://arxiv.org/abs/2402.08855)