Enhance dataset card for ReasonMap-Plus: Add paper, links, usage, abstract, citation

README.md

@@ -1,3 +1,99 @@
 ---
 license: apache-2.0
----
+task_categories:
+  - image-text-to-text
+tags:
+  - multimodal
+  - visual-question-answering
+  - spatial-reasoning
+  - reinforcement-learning
+  - transit-maps
+language:
+  - en
+---
+
+# ReasonMap-Plus Dataset
+
+This repository hosts the `ReasonMap-Plus` dataset, 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).
+
+## Paper Abstract
+Fine-grained visual reasoning remains a core challenge for multimodal large language models (MLLMs). The recently introduced ReasonMap highlights this gap by showing that even advanced MLLMs struggle with spatial reasoning in structured and information-rich settings such as transit maps, a task of clear practical and scientific importance. However, standard reinforcement learning (RL) on such tasks is impeded by sparse rewards and unstable optimization. To address this, we first construct ReasonMap-Plus, an extended dataset that introduces dense reward signals through Visual Question Answering (VQA) tasks, enabling effective cold-start training of fine-grained visual understanding skills. Next, we propose RewardMap, a multi-stage RL framework designed to improve both visual understanding and reasoning capabilities of MLLMs. RewardMap incorporates two key designs. First, we introduce a difficulty-aware reward design that incorporates detail rewards, directly tackling the sparse rewards while providing richer supervision. Second, we propose a multi-stage RL scheme that bootstraps training from simple perception to complex reasoning tasks, offering a more effective cold-start strategy than conventional Supervised Fine-Tuning (SFT). Experiments on ReasonMap and ReasonMap-Plus demonstrate that each component of RewardMap contributes to consistent performance gains, while their combination yields the best results. Moreover, models trained with RewardMap achieve an average improvement of 3.47% across 6 benchmarks spanning spatial reasoning, fine-grained visual reasoning, and general tasks beyond transit maps, underscoring enhanced visual understanding and reasoning capabilities.
+
+## Dataset Overview
+`ReasonMap-Plus` addresses the core challenge of fine-grained visual reasoning for multimodal large language models (MLLMs). It extends the original `ReasonMap` dataset by introducing dense reward signals through Visual Question Answering (VQA) tasks, enabling effective cold-start training of fine-grained visual understanding skills. This dataset is crucial for the `RewardMap` framework, which aims to improve both visual understanding and reasoning capabilities of MLLMs in structured and information-rich settings like transit maps.
+
+The dataset includes `ReasonMap-Plus` for evaluation and `ReasonMap-Train` for `RewardMap` training.
+
+## Links
+-   **Project Page:** [https://fscdc.github.io/RewardMap](https://fscdc.github.io/RewardMap)
+-   **Code Repository:** [https://github.com/fscdc/RewardMap](https://github.com/fscdc/RewardMap)
+
+<p align="center">
+<img src="https://github.com/fscdc/RewardMap/raw/main/assets/rewardmap.svg" width = "95%" alt="RewardMap Framework Overview" align=center />
+</p>
+
+## Sample Usage
+
+To get started with the RewardMap project and utilize the ReasonMap-Plus dataset, follow the steps below.
+
+### 1. Install dependencies
+
+If you face any issues with the installation, please feel free to open an issue. We will try our best to help you.
+
+```bash
+pip install -r requirements.txt
+```
+
+### 2. Download the dataset
+
+<p align="center">
+<img src="https://github.com/fscdc/RewardMap/raw/main/assets/overview_dataset.svg" width = "95%" alt="Dataset Overview" align=center />
+</p>
+
+You can download `ReasonMap-Plus` for evaluation and `ReasonMap-Train` for RewardMap Training from HuggingFace or by running the following command:
+
+```bash
+python utils/download_dataset.py
+```
+
+Then, put the data under the folder `data`.
+
+### 3. Data Format Example
+
+The data will be transferred into a format like:
+
+```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"
+    ]
+  },
+```
+
+## 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}
+}
+```