Create README.md

README.md

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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}
+}
+```