VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning
Score
78提出新测试套件与可验证奖励机制，填补了原生视觉推理训练与评估的空白。
方法系统完整，包含大规模实验与机制分析，论证扎实且资源开源。
开源数据、模型与评分器，奖励信号可直接用于视觉强化学习，便于复现和扩展。
紧扣视觉推理与生成模型的前沿热点，预印本发布及时。
来源为 arXiv 预印本，尚未经同行评审，但作者公开了完整资源。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper is information-dense and methodologically complete, covering task design, reward mechanisms, and modality studies, though it is a preprint without peer review.
AI commentary
「This paper turns “generation-as-reasoning” from a slogan into an experimental system, with task scaling, reliable rewards, and modality ablations all in one package. It is especially valuable for researchers working on visual RL, multimodal evaluation, and generative reasoning, and the released assets could become a new community baseline.」
Quick overview
VBVR-Pro is a closed-loop testbed for native visual reasoning, treating visual generation itself as the medium of reasoning. It features 300 procedurally generated tasks that enable scalable training, with models showing strong transfer to seven external benchmarks including RISE-Video, MME-CoF-Pro, and BabyVision. It provides verifiable reward scorers grounded in deterministic task-specific rules, overcoming common failure modes of the VLM-as-a-judge paradigm and serving as reliable reward signals for large-scale multi-task reinforcement learning. Furthermore, it enables controlled comparisons across over 30 image, video, and interleaved generators, showing that video generation is strongest for persistent spatiotemporal state tracking while interleaved generation is a compute-efficient alternative. All data, models, scorers, and code are released.
- A controlled task space of 300 procedurally generated tasks enables scalable training, with strong transfer to seven external benchmarks such as RISE-Video, MME-CoF-Pro, and BabyVision.
- Verifiable reward scorers grounded in deterministic rules outperform the prevalent VLM-as-a-judge paradigm and provide reliable signals for large-scale multi-task reinforcement learning.
- Controlled modality studies across more than 30 image, video, and interleaved generators show video generation excels at persistent spatiotemporal state tracking, while interleaved generation is more compute-efficient.
- All data, models, scorers, and code are publicly released.
Full text
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