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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 (seereadme.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_kdetourresults, 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:
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
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