---
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`). |