VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning
Summary
VBVR-Pro introduces a closed-loop testbed for scalable and verifiable native visual reasoning through generation, featuring task scaling, verifiable rewards, and mechanism studies across diverse visual substrates.
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Paper page - VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning
Source: https://huggingface.co/papers/2608.26105 Published on Aug 26
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Abstract
VBBR-Pro introduces a closed-loop testbed that enables scalable, verifiable, and controllable native visual reasoning through generation across diverse visual substrates.
Native visual reasoningtreatsvisual generationas the medium of reasoning itself: visual states (i.e. images and videos) are not merely inputs to be understood or outputs to be rendered, but first-class substrates for problem solving beyond language. Yet progress remains bottlenecked by the lack of scalable training tasks, reliable feedback, and controlled comparisons across generative substrates. In this work, we introduce VBVR-Pro, a closed-loop testbed that makesnative visual reasoningthrough generation trainable, verifiable, optimizable, and experimentally controllable. 1) Task scaling. VBVR-Pro turns visual reasoning into a controlled task space of 300 procedurally generated tasks. Models trained on VBVR-Pro show strong transfer beyond the proposed suite across seven external visual reasoning benchmarks such as RISE-Video, MME-CoF-Pro, and BabyVision. 2) Verifiable rewards. VBVR-Pro providesverifiable reward scorersfor task-grounded evaluation. Through a systematic study of leadingMLLMsas judges, we identify recurring failure modes of the prevalentVLM-as-a-judgeparadigm. In contrast, the proposed scorers are grounded in deterministic, task-specific rules, achieve fine-grained alignment with human judgments. Importantly, they serve as reliable reward signals for large-scalemulti-task reinforcement learningand demonstrate stronger post-RL performance across visual reasoning tasks. 3) Mechanism study. VBVR-Pro enables controlled modality studies across more than 30 image, video, and interleaved generators. Our analysis shows that video generation remains strongest for tasks requiring persistent spatiotemporal state tracking, whileinterleaved generationprovides a compute-efficient alternative. Critically, ablations and probing suggest the presence ofvision-native trajectoriesthat are crucial to visual reasoning. We release all data, models, scorers, and code.
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