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datasets:
- Fish-03/RuleMaze
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
library_name: peft
tags:
- multimodal
- vision-language
- visual-planning
- spatial-planning
- rule-following
- qwen2.5-vl
- lora
pipeline_tag: image-text-to-text
---
# RuleMaze
RuleMaze is a benchmark and training framework for **rule-compliant visual spatial planning** with Multimodal Large Language Models (MLLMs).
Given a visual maze and a set of natural-language rules, the model is required to understand the environment, follow the rules, and generate a valid multi-step trajectory.
This repository provides LoRA adapters fine-tuned from **Qwen2.5-VL-3B-Instruct** on the RuleMaze training data.
## Checkpoints
Two scene types are provided:
* `RuleMaze/regular/checkpoint`
* `RuleMaze/quest/checkpoint`
The checkpoints are PEFT/LoRA adapters and should be loaded together with the base model:
```text
Qwen/Qwen2.5-VL-3B-Instruct
```
## Resources
* **Code:** https://github.com/oceanflowlab/RuleMaze
* **Dataset:** https://huggingface.co/datasets/Fish-03/RuleMaze
* **Base Model:** https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct
## Training and Evaluation
The models are trained using the RuleMaze DMP training pipeline with LLaMA-Factory.
RuleMaze evaluates visual planning under both **seen-rule** and **unseen-rule** settings and different rule difficulties.
For training and evaluation details, please refer to the official code repository.
## Intended Use
The models are intended for research on:
* multimodal reasoning
* visual spatial planning
* rule following
* compositional generalization
## Citation
If you find RuleMaze useful, please cite:
```bibtex
@misc{rulemaze,
title = {Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models},
author = {Yu Chen, Ting Lei, Yaoyi Li, Jia Cai, Zhecen Wu and Yang Liu},
year = {2026},
note = {Code and dataset release}
}
``` |