---
pretty_name: SPARC VQA
tags:
  - robotics
  - vision-language
  - spatial-reasoning
  - sparc
---

# SPARC VQA

SPARC VQA is the generated spatial VQA training dataset used in the SPARC Qwen3.5 model releases. Each example embeds its image bytes and includes a question, answer, task type, target type, source dataset identifier, split, and JSON metadata.

**Raw unfiltered corpus:** https://huggingface.co/datasets/irl-kit/SPARC-VQA-Raw

## Ready-to-train split

Use `train_filtered_t097_mpo700.parquet` for SPARC-only training. This is the processed, release-ready dataset: it contains the exact 284,909 examples retained by the release configuration and needs no further SPARC filtering:

- `selected_start_score >= 0.97`
- sorted by `selected_start_score` in descending order before diversity selection
- a maximum of 700 `(source, trajectory_name, subtask_index)` groups per normalized object name
- vacant-goal and vacant-start examples mentioning `gripper` excluded

For the unfiltered 838,211-example source corpus, use [irl-kit/SPARC-VQA-Raw](https://huggingface.co/datasets/irl-kit/SPARC-VQA-Raw). The raw repository contains `train.parquet`, the exact `export_sparc_training_subset.py` postprocessing script, `release_mixture.yaml`, and instructions for obtaining this processed dataset or creating a custom filtered variant.

## Data schema

| Field | Description |
| --- | --- |
| `sample_id` | Stable example identifier |
| `image` | Embedded image bytes and optional path |
| `question` | User text prompt |
| `answer` | Supervised assistant answer |
| `target_type`, `task_type` | Spatial target and VQA task labels |
| `source`, `split`, `metadata` | Provenance and generation metadata |

## Mixtures

| Release | Data mixture |
| --- | --- |
| Qwen3.5-4B | SPARC VQA + FSD + RoboPoint + LLaVA-OneVision2 |
| Qwen3.5-0.8B-VTFT | SPARC VQA + FSD + RoboPoint + LLaVA-OneVision2 |
| Qwen3.5-9B-EO | SPARC VQA + FSD + RoboPoint + LLaVA-OneVision2 + EO-1.5M |

FSD, RoboPoint, LLaVA-OneVision2, and EO-1.5M are external datasets and should be retrieved from their upstream releases under their respective terms. The released models are [Qwen3.5-4B](https://huggingface.co/irl-kit/SPARC-Qwen3.5-4B), [Qwen3.5-0.8B-VTFT](https://huggingface.co/irl-kit/SPARC-Qwen3.5-0.8B-VTFT), and [Qwen3.5-9B-EO](https://huggingface.co/irl-kit/SPARC-Qwen3.5-9B-EO).

## Prompting compatibility

The released models are sensitive to output formatting. Use each model's bundled chat template with a single user turn containing the image followed by the question. For point prediction, use this suffix:

```text
Output the point coordinates in JSON format like [{"point_2d": [x, y], "label": "target"}]. Use integer coordinates between 0 and 1000.
```

For trajectories or multiple points, use this suffix:

```text
Return only a JSON list like [{"point_2d": [x1, y1], "label": "point_1"}, {"point_2d": [x2, y2], "label": "point_2"}, ...]. Use integer coordinates between 0 and 1000.
```

## Citation

```bibtex
@article{blank2026sparc,
  title={SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale},
  author={Blank, Nils and others},
  journal={arXiv preprint arXiv:2606.13497},
  year={2026}
}
```