SAVE: Sufficiency-Aware Visual Evidence Acquisition for Multimodal Reasoning

Visual tools allow multimodal large language models to acquire additional visual evidence during reasoning, but effective tool use requires judging whether the available evidence is sufficient. Existing models may miss useful inspection or continue interacting after sufficient evidence has already been obtained. We therefore formulate adaptive visual tool use as an evidence sufficiency problem with two decisions: whether to acquire additional evidence and when to stop. To learn these decisions from complete-trajectory feedback, we introduce SAVE, a Sufficiency-aware framework for Adaptive Visual Evidence acquisition. For each question, SAVE samples multiple on-policy trajectories and constructs a hierarchical reward at two comparison scopes. Across direct-answer and tool-assisted strategies, it strengthens successful trajectories when observed success occurs in only one strategy; within successful tool-assisted trajectories, it favors the minimum observed successful acquisition depth. This design learns invocation and stopping preferences without estimating the marginal utility of individual tool calls or imposing a uniform per-call cost. Across three vision-language backbones and five multimodal reasoning benchmarks, SAVE improves Avg@3 over supervised fine-tuning by 8.33 percentage points on average while reducing the macro-average number of calls per tool-using trajectory by 8.6%.