Learning Iterative Reasoning through Energy Diffusion

Pytorch implementation for the Iterative Reasoning Energy Diffusion (IRED).

Learning Iterative Reasoning through Energy Diffusion Yilun Du*, Jiayuan Mao*, and Joshua B. Tenenbaum In International Conference on Machine Learning (ICML), 2024 [Paper] [Project Page]

@InProceedings{Du_2024_ICML,
    author    = {Du, Yilun and Mao, Jiayuan and Tenenbaum, Joshua B.},
    title     = {Learning Iterative Reasoning through Energy Diffusion},
    booktitle = {International Conference on Machine Learning (ICML)},
    year      = {2024}
}

Continuous-Space Reasoning Tasks

python3 train.py --dataset addition --data-workers 4 --batch_size 2048 --use-innerloop-opt True --supervise-energy-landscape True
python3 train.py --dataset lowrank --data-workers 4 --batch_size 2048 --use-innerloop-opt True --supervise-energy-landscape True
python3 train.py --dataset inverse --data-workers 4 --batch_size 2048 --use-innerloop-opt True --supervise-energy-landscape True

Discrete-Space Reasoning Tasks

python3 train.py --dataset sudoku --batch_size 64 --model sudoku   --cond_mask True  --supervise-energy-landscape True   --use-innerloop-opt True
python3 train.py --dataset connectivity-2 --batch_size 512 --model gnn --data-workers 20 --use-innerloop-opt True --supervise-energy-landscape True

Planning Tasks

python3 ./gen_planning_dataset.py shortest-path --size 100000   # takes around 10 mins.
python3 ./gen_planning_dataset.py shortest-path-25 --size 10000 # takes around 2mins.
python train.py --dataset shortest-path-1d --model gnn-conv-1d-v2 --data-workers 2 --batch_size 512 --use-innerloop-opt True --supervise-energy-landscape True