Add comprehensive dataset card for ReasonMap-Plus

This PR adds a comprehensive dataset card for the `ReasonMap-Plus` dataset.

Key additions and improvements include:
- Populating relevant metadata, including `task_categories` (`image-text-to-text`, `visual-question-answering`), `language` (`en`), and relevant `tags` (`visual-question-answering`, `multimodal`, `reinforcement-learning`, `visual-reasoning`, `spatial-reasoning`, `transit-maps`).
- A concise introductory description of the dataset, derived from the paper's abstract.
- Links to the associated paper, project page, and GitHub repository.
- A detailed "Sample Usage" section, directly incorporating code snippets from the GitHub README for dependency installation, dataset download, data preparation for SFT, a data format example, and a training command.
- The official BibTeX citation for the paper.

This update significantly improves the discoverability and usability of the `ReasonMap-Plus` dataset on the Hugging Face Hub.

README.md

@@ -0,0 +1,101 @@
+---
+task_categories:
+- image-text-to-text
+- visual-question-answering
+language:
+- en
+tags:
+- visual-question-answering
+- multimodal
+- reinforcement-learning
+- visual-reasoning
+- spatial-reasoning
+- transit-maps
+---
+
+# ReasonMap-Plus Dataset
+
+The **ReasonMap-Plus** dataset is an extended dataset introduced in the paper [RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning](https://huggingface.co/papers/2510.02240). It addresses the challenge of sparse rewards in fine-grained visual reasoning for multimodal large language models (MLLMs), particularly in structured and information-rich settings like transit maps. ReasonMap-Plus achieves this by introducing dense reward signals through Visual Question Answering (VQA) tasks, which enables effective cold-start training of fine-grained visual understanding skills. This repository contains the `ReasonMap-Plus` data for evaluation and `ReasonMap-Train` for RewardMap training.
+
+- **Paper:** [RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning](https://huggingface.co/papers/2510.02240)
+- **Project Page:** https://fscdc.github.io/RewardMap
+- **Code:** https://github.com/fscdc/RewardMap
+
+## Sample Usage
+
+To get started with the `ReasonMap-Plus` dataset, follow these steps to install dependencies, download the data, and prepare it for training.
+
+### 1. Install dependencies
+
+If you face any issues with the installation, please feel free to open an issue on the GitHub repository.
+
+```bash
+pip install -r requirements.txt
+```
+
+### 2. Download the dataset
+
+You can download [ReasonMap-Plus](https://huggingface.co/datasets/FSCCS/ReasonMap-Plus) (for evaluation) and [ReasonMap-Train](https://huggingface.co/datasets/FSCCS/ReasonMap-Train) (for RewardMap training) from Hugging Face or by running the following command:
+
+```bash
+python utils/download_dataset.py
+```
+
+Then, put the data under the folder `data`.
+
+### 3. Prepare data for Supervised Fine-Tuning (SFT)
+
+If you plan to use tools like [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) for SFT training, first prepare the datasets by running the following command:
+
+```bash
+python utils/prepare_data_for_sft.py --dataset_dir path/to/your_data
+```
+
+### 4. Data Format Example
+
+Your data will be transferred into a format similar to this for SFT:
+
+```json
+  {
+    "conversations": [
+      {
+        "from": "human",
+        "value": "<image> Please solve the multiple choice problem and put your answer (one of ABCD) in one \"\\boxed{}\". According to the subway map, how many intermediate stops are there between Danube Station and lbn Battuta Station (except for this two stops)? \
+A) 8 \
+B) 1 \
+C) 25 \
+D) 12 \
+"
+      },
+      {
+        "from": "gpt",
+        "value": "B"
+      }
+    ],
+    "images": [
+      "./maps/united_arab_emirates/dubai.png"
+    ]
+  },
+```
+
+### 5. Training Example
+
+You can train the `RewardMap` model using the provided scripts:
+
+```bash
+# RewardMap training
+bash scripts/reward_map.sh
+```
+
+## Citation
+
+If you find this paper useful in your research, please consider citing our paper:
+
+```bibtex
+@article{feng2025rewardmap,
+  title={RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning},
+  author={Feng, Sicheng and Tuo, Kaiwen and Wang, Song and Kong, Lingdong and Zhu, Jianke and Wang, Huan},
+  journal={arXiv preprint arXiv:2510.02240},
+  year={2025}
+}
+```