1. Overview of ViRL39K

ViRL39K (pronounced as "viral") provides a curated collection of 38,870 verifiable QAs for Vision-Language RL training. It is built on top of newly collected problems and existing datasets ( Llava-OneVision, R1-OneVision, MM-Eureka, MM-Math, M3CoT, DeepScaleR, MV-Math) through cleaning, reformatting, rephrasing and verification.

ViRL39K lays the foundation for SoTA Vision-Language Reasoning Model VL-Rethinker. It has the following merits:

• high-quality and verifiable: the QAs undergo rigorous filtering and quality control, removing problematic queries or ones that cannot be verified by rules.
• covering comprehensive topics and categories: from grade school problems to broader STEM and Social topics; reasoning with charts, diagrams, tables, documents, spatial relationships, etc.
• with fine-grained model-capability annotations: it tells you what queries to use when training models at different scales.

Explore more about VL-Rethinker:

• Project Page
• Github
• Paper
• Models

2. Dataset Statistics

2.1 ViRL39K covers eight major categories:

2.2 ViRL39K covers different difficulty levels for different model scales.

We associate each query with a PassRate annotation that reflects model-capability affinity.

You can use this annotation to select the proper queries to train models at different scales.

3. Dataset Keys

• answer: all answers are with \boxed{}.

For answer extractions, we recommend using the math-verify library.

It can handle partial match where the answer has text in it, such as : predicted = \\boxed{17}, answer = \\boxed{17^\circ}.

You can refer to our Github for reference of extraction and matching functions.

• PassRate:

we provide all PassRate for 32BTrained, but provide only partial PassRate for 7BUntrained, to save compute.

Specifically, we only label PassRate on 7BUntrained with 50% queries in the dataset. These selected queries are easy for 32BTrained, which has PassRate==1.0.

The remaining queries are somewhat challenging for 32BTrained (PassRate<1.0), so we assume they will also be challenging for 7BUntrained.

Note: For 7BUntrained PassRate annotations, if they are not tested because PassRate_32BTrained<1.0, they are labeled PassRate_7BUntrained=-1.0.

• Category:

you can choose queries of interest based on the category.

Citation

If you find ViRL39K useful, please give us a free cit:

@article{vl-rethinker,
      title={VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning},
      author = {Wang, Haozhe and Qu, Chao and Huang, Zuming and Chu, Wei and Lin,Fangzhen and Chen, Wenhu},
      journal={arXiv preprint arXiv:2504.08837},
      year={2025}
}