---
license: mit
task_categories:
  - other
tags:
  - maze
  - world-model
  - sequence-modeling
  - transformer
  - mamba
pretty_name: World Model For Maze
---

# WorldModelForMaze

Code, datasets, and trained checkpoints for studying world-model representations in maze navigation, based on a modified NanoGPT.

## Contents

- `*.py` — training, testing, probing, and visualization scripts (see `readme.md`).
- `model/` — architectures: transformer, transformer-rope, transformer-nextlat, mamba, mamba2, gated-deltanet, gru.
- `data/maze/100/` — tokenized maze datasets for Tasks A/C/E/H/I (RWs paths, 100 nodes).
- `out/` — final (10000-iter) checkpoints, `maze_kdetour` results, and plots.

> Intermediate (non-10000-iter) checkpoints (`out2/`) are not included here.

## Usage

See `readme.md` for full instructions on data generation, training, testing, probing, and the k-step detour analysis.

Quick start:

```bash
git clone https://huggingface.co/datasets/Kalso42/WorldModelForMaze
cd WorldModelForMaze
python train_maze.py --tasks C1 --path_type RWs --num_train_dataset 10M --config 6_6_384
```