File size: 1,523 Bytes
6275f89 c42a0dd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
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
|