给AI换双眼睛：只看该看的地方，省4倍算力还更准
现在的多模态大模型看高清图，是把整张图切成小块全看一遍，又慢又费算力。这篇论文发现一个反直觉的事：模型找“该看哪”和“看清那是什么”对清晰度的要求不一样——定位可以容忍3到4倍的压缩，识别才需要高清。于是他们让模型先用低清图快速圈出重点区域，再把高清算力全砸在那一小块上，并用强化学习让圈选区域直接对最终答案负责。结果在6个精细感知基准上，每个算力预算下准确率都更高，用约1/4的视觉token就超过了原来最高清配置的准确率。这不是你明天能直接用的功能，但它指向一个趋势：AI看图的下一步不是无脑堆像素，而是学会像人一样“扫一眼再细看”。
📄 原文摘要(英文)
Fine-grained visual perception in MLLMs is commonly improved by raising the resolution, but the added visual tokens inflate vision-encoding and language-model prefilling costs. We show that the two operations underlying fine-grained perception, localizing the region of interest (RoI) and recognizing its content, have different resolution requirements. In a controlled diagnostic, localization tolerates roughly 3 to 4 times stronger token compression than recognition, which motivates localizing from a coarse view and concentrating resolution on the selected evidence. Decoding coordinates with the MLLM can be trained end-to-end from answers, but costs a full model pass per query and depends on grounding ability. A lightweight proposal network distilled from the model's attention is fast, but inherits the noise of its attention targets. The RoI from the proposal network reaches the answer through a discrete region choice, so its faithfulness to the answer cannot supervise the network. We therefore optimize the proposal network with region-level reinforcement learning, which we call Vision-RL2. It treats coherent regions as actions, and a frozen MLLM reader scores each one by how its removal changes the answer likelihood. Complementary subtractive and additive objectives suppress distracting proposals and recover missing evidence, updating only the predictor without region annotations, response sampling, or reasoning trajectories. The refined proposal further enables a sparse encoding that magnifies evidence and excludes background tokens. Across six fine-grained benchmarks and four MLLM backbones, Vision-RL2 improves accuracy over the base model at every token budget and surpasses its largest-budget accuracy with about 4 times fewer visual tokens. Code is available at https://github.com/YuHengsss/VisionRL2 .