--- 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)

RewardMap Framework Overview

## 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

Dataset Overview

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": " 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} } ```