README.md

---
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}
}
```