# Diffusion for World Modeling: Visual Details Matter in Atari

Authors: Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos Storkey, Tim Pearce, François Fleuret.

Introduces DIAMOND, an agent trained in a diffusion world model, and studies how preserving visual detail affects control.

First public: 2024-05-20.
ArXiv initial submission: 2024-05-20.

[Original paper](https://arxiv.org/abs/2405.12399) · [Reading-list entry](https://neural-os.com/reading-list/papers/2405.12399/)
[Preprint BibTeX](https://neural-os.com/reading-list/papers/2405.12399/citation.bib) · [Preprint CSL-JSON](https://neural-os.com/reading-list/papers/2405.12399/citation.csl.json) · [JSON record](https://neural-os.com/reading-list/papers/2405.12399/record.json)

**Contribution:** Connects diffusion-based observation prediction with Atari policy learning and demonstrates an interactive Counter-Strike simulator.

**Model output:** Predicted visual observations; auxiliary reward and termination predictions support reinforcement learning.

**Action interface:** Actions of the modeled game environment.

**State and memory:** Visual dynamics condition on a finite history of observations and actions.

**Relationship to NeuralOS:** NeuralOS adapts DIAMOND to OS screen prediction as a baseline in Appendix A of its 2026 revision.

**Scope and limitations:** Evaluated on games; NeuralOS adapts and evaluates it for desktop screen prediction.

[Relationship source](https://arxiv.org/html/2507.08800v2#A1)

Related terms: DIAMOND, diffusion world model, neural game engine, model-based reinforcement learning.

## Method comparisons

**A game world model evaluated on desktop interactions**

DIAMOND learns visual game dynamics with a diffusion model. NeuralOS adapts DIAMOND to OS screen prediction as a baseline in Appendix A of its 2026 revision. NeuralOS adds a recurrent state representation and spatial cursor conditioning for desktop interaction.

Sources: [DIAMOND paper](https://arxiv.org/abs/2405.12399) · [NeuralOS Appendix A](https://arxiv.org/html/2507.08800v2#A1)

Paper details: [Diffusion for World Modeling: Visual Details Matter in Atari](https://neural-os.com/reading-list/papers/2405.12399/) · [NeuralOS: Towards Simulating Operating Systems via Neural Generative Models](https://neural-os.com/reading-list/papers/2507.08800/)


## Approaches

- [Interactive learned environments](https://neural-os.com/reading-list/guides/games-to-computers/): Learn how an environment changes after actions, then generate observations during continued interaction. Compare visual fidelity, control interfaces, memory, and the reliability of repeated predictions.
- [Environments and evaluation](https://neural-os.com/reading-list/guides/evaluation/): Test action response, visual quality, remembered state, and completed tasks separately. Real desktop and mobile environments provide execution-based checks that complement learned-simulator measurements.

Reading-list version 1.0.1; literature checked through 2026-10-19.