|
|
--- |
|
|
language: en |
|
|
license: mit |
|
|
tags: |
|
|
- trm |
|
|
- recursive-reasoning |
|
|
- maze-solving |
|
|
- pytorch |
|
|
- huggingface |
|
|
datasets: |
|
|
- custom |
|
|
metrics: |
|
|
- accuracy |
|
|
widget: |
|
|
- text: "Sample maze input here" |
|
|
--- |
|
|
|
|
|
# TRM Model for Maze Solving |
|
|
|
|
|
## Model Description |
|
|
|
|
|
This is a Tiny Recursive Model (TRM) fine-tuned for solving maze navigation tasks. The model implements recursive reasoning to find paths in 30x30 grid mazes. |
|
|
|
|
|
- **Developed by:** alphaXiv |
|
|
- **Model type:** TRM-Attention |
|
|
- **Language(s) (NLP):** N/A (grid-based reasoning) |
|
|
- **License:** MIT |
|
|
- **Finetuned from model:** Custom TRM architecture |
|
|
|
|
|
## Intended Use |
|
|
|
|
|
### Primary Use |
|
|
|
|
|
This model is designed to solve maze pathfinding problems by predicting the correct sequence of moves to navigate from start to goal in grid-based mazes. |
|
|
|
|
|
### Out-of-Scope Use |
|
|
|
|
|
Not intended for general NLP tasks, image classification, or other domains outside maze solving. |
|
|
|
|
|
## Limitations and Bias |
|
|
|
|
|
- Trained only on synthetic maze data |
|
|
- May not generalize to mazes of different sizes or complexities |
|
|
- Performance may degrade on mazes with unusual patterns |
|
|
|
|
|
## Training Data |
|
|
|
|
|
The model was trained on a dataset of 30x30 grid mazes with hard difficulty levels. The dataset includes: |
|
|
- Start and goal positions |
|
|
- Wall configurations |
|
|
- Correct path sequences |
|
|
|
|
|
|
|
|
|
|
|
## Evaluation Results |
|
|
|
|
|
| Metric | Claimed | Achieved | |
|
|
|--------|---------|----------| |
|
|
| Exact Accuracy | 85.3% | 83.67% ± 2.28% | |
|
|
|
|
|
Results from independent reproduction study. |
|
|
|
|
|
## Repository |
|
|
|
|
|
https://github.com/alphaXiv/TinyRecursiveModels |
|
|
|
