VTTS: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception
Ziang Yan, Yinan He, Xinhao Li, Zhengrong Yue, Xiangyu Zeng, Yali Wang, Yu Qiao, Limin Wang, Yi Wang
Inducing reasoning in multimodal large language models (MLLMs) is critical for achieving human-level perception and understanding. Existing methods mainly leverage LLM reasoning to analyze parsed visuals, often limited by static perception stages. This paper introduces Visual Test-Time Scaling (VTTS), a novel approach to enhance MLLMs' reasoning via iterative perception during inference. VTTS mimics humans' hierarchical attention by progressively refining focus on high-confidence spatio-temporal regions, guided by updated textual predictions. Specifically, VTTS employs an Iterative Perception (ITP) mechanism, incorporating reinforcement learning with spatio-temporal supervision to optimize reasoning. To support this paradigm, we also present VTTS-80K, a dataset tailored for iterative perception.These designs allows a MLLM to enhance its performance by increasing its perceptual compute.  Extensive experiments validate VTTS's effectiveness and generalization across diverse tasks and benchmarks. It exhibits substantial improvements (by more than 5\% on average) over strong baselines (Qwen2.5VL-3B and -7B) on more than 20 benchmarks covering video conversation, image reasoning, and spatio-temporal perception.