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Kalso42/WorldModelForMaze

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)… See the full description on the dataset page: https://huggingface.co/datasets/Kalso42/WorldModelForMaze.

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Dataset Card

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