StAR: Segment Anything Reasoner

As AI systems are being integrated more rapidly into di-verse and complex real-world environments, the ability to perform holis-tic reasoning over an implicit query and an image to localize a target isbecoming increasingly important. However, recent reasoning segmenta-tion methods fail to sufficiently elicit the visual reasoning capabilities ofthe base model. In this work, we present Segment Anything Reasoner(StAR), a comprehensive framework that refines the design space frommultiple perspectives—including parameter-tuning scheme, reward func-tions, learning strategies and answer format—and achieves substantialimprovements over recent baselines. In addition, for the first time, wesuccessfully introduce parallel test-time scaling to the segmentation task,pushing the performance boundary even further. To eXtend the scopeand depth of reasoning covered by existing benchmark, we also con-struct the ReasonSeg-X, which compactly defines reasoning types and in-cludes samples that require deeper reasoning. Leveraging this dataset, wetrain StAR with a rollout-expanded selective-tuning approach to activatethe base model’s latent reasoning capabilities, and establish a rigorousbenchmark for systematic, fine-grained evaluation of advanced methods.With only 5k training samples, StAR achieves significant gains over itsbase counterparts across extensive benchmarks, demonstrating that ourmethod effectively brings dormant reasoning competence to the surface.