| --- |
| 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} |
| } |
| ``` |
