Learning Iterative Reasoning through Energy Minimization
Authors: Yilun Du, Shuang Li, Joshua Tenenbaum, Igor Mordatch
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically illustrate that our iterative reasoning approach can solve more accurate and generalizable algorithmic reasoning tasks in both graph and continuous domains. and 4. Experiments |
| Researcher Affiliation | Collaboration | Yilun Du 1 Shuang Li 1 Joshua Tenenbaum 1 Igor Mordatch 2 1MIT CSAIL 2Google Brain. |
| Pseudocode | Yes | We provide pseudocode for training IREM in Algorithm 1 and executing algorithmic reasoning with IREM our approach in Algorithm 2... and Algorithm 1 IREM training algorithm, Algorithm 2 IREM prediction algorithm, Algorithm 3 IREM Training with External Memory. |
| Open Source Code | Yes | Code and additional information is available at https://energy-based-model.github.io/iterativereasoning-as-energy-minimization/. |
| Open Datasets | No | The paper describes generating its own datasets (e.g., 'We randomly sample a value for each edge...', 'We randomly construct two separate vectors...') and provides details on their construction in Appendix C, but does not provide specific access information (link, DOI, formal citation) to make these datasets publicly available. |
| Dataset Splits | No | The paper specifies training on certain graph sizes (e.g., 'size 2 to 10') and testing on larger/harder problems (e.g., 'size 15', 'larger magnitudes'), but it does not explicitly state the use of a validation set or specific training/validation/test splits (e.g., 80/10/10 split). |
| Hardware Specification | Yes | Models were trained in approximately 2 hours on a single Nvidia Titan X GPU using a training batch size of 64 and the Adam optimizer with learning rate 1e-4. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'GINEConv layer', but does not specify their version numbers (e.g., PyTorch 1.9, TensorFlow 2.x, Python 3.x). |
| Experiment Setup | Yes | Models were trained in approximately 2 hours on a single Nvidia Titan X GPU using a training batch size of 64 and the Adam optimizer with learning rate 1e-4. Each model was trained for 10,000 iterations and evaluated on 1000 test problems. and Each model was trained with five steps of iterative computation, with Ponder Net trained with a halting geometric distribution of 0.8. |