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