| --- |
| license: apache-2.0 |
| task_categories: |
| - visual-question-answering |
| - object-detection |
| tags: |
| - multimodal |
| - RL |
| - vision-agent |
| - tool-learning |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: reasoning_rl.parquet |
| features: |
| - name: data_source |
| dtype: string |
| - name: prompt |
| list: |
| - name: content |
| dtype: string |
| - name: role |
| dtype: string |
| - name: images |
| list: |
| - name: bytes |
| dtype: binary |
| - name: name |
| dtype: string |
| - name: path |
| dtype: string |
| - name: ability |
| dtype: string |
| - name: env_name |
| dtype: string |
| - name: reward_model |
| dtype: string |
| - name: extra_info |
| dtype: string |
| - name: agent_name |
| dtype: string |
| --- |
| --- |
|
|
| # APEX-GRO-RL Dataset |
|
|
| ## 1. Introduction |
| `APEX-GRO-RL` is a multimodal dataset specifically curated for training **Visual Analysis Agents** using Reinforcement Learning (RL). It integrates visual counting and visual grounding tasks, designed to teach agents how to autonomously plan reasoning behaviors and invoke active perception tools (such as `zoom_in`) to inspect dense or small targets in high-resolution images. |
|
|
| The data format seamlessly fits training environments like `visual_toolbox`, where system observations and structured tool-call formatting are required. |
|
|
| ## 2. Dataset Structure |
|
|
| The dataset is stored in Apache Parquet format. Each entry contains the following fields: |
|
|
| | Field Name | Type | Description | |
| | :--- | :--- | :--- | |
| | `data_source` | string | Source of the original data (`APEX-GRO`). | |
| | `prompt` | list | Multi-turn style conversational prompt template containing `system` guidelines and the formatted `user` question. | |
| | `images` | list | List of images related to the sample. Each image dict contains `name`, `path`, and raw image binary data encoded in **WebP** format. | |
| | `ability` | string | Task capability type: `counting` or `grounding`. | |
| | `env_name` | string | Target environment name for RL setup (`visual_toolbox`). | |
| | `reward_model` | string (JSON) | Configuration for reward calculation, including `ground_truth` and matching `style`. | |
| | `extra_info` | string (JSON) | Metadata tracking including original dataset index, original resolution, and target relative bounding boxes (`rel_bboxs`). | |
| | `agent_name` | string | Target agent architecture type (`tool_agent`). | |
|
|
