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