AI看图推理，不再需要人工标注答案
训练AI做细粒度视觉推理（比如“图中哪只猫在追老鼠？”），通常需要大量人工标注的答案或复杂的奖励规则。这篇论文发现，让AI自己生成推理过程、再自我纠正（在线蒸馏）虽然能修正单词错误，但缺乏对整条推理路径好坏的判断。于是他们加了一个“对比证据门”：给AI看一张相关区域的正例和一张无关区域的负例，让它自己判断哪条推理路径更靠谱。结果，训练速度比传统监督学习快5倍，比强化学习快10倍，且不需要任何人工答案标注。它不是你明天就能直接用的工具，但指向了一个更高效、更少依赖人工的AI训练方向。
📄 原文摘要(英文)
Fine-grained visual reasoning requires multimodal large language models (MLLMs) to identify task-relevant visual evidence and ground their reasoning in local image regions. Existing agentic methods typically rely on reinforcement learning with verifiable rewards or supervised fine-tuning on large-scale annotated reasoning traces, leading to costly exploration, hand-designed verification rules, or heavy dependence on textual supervision. A natural way to avoid such external answer labels is to learn from trajectories sampled by the student itself, which points to On-Policy Distillation (OPD). To understand what OPD can and cannot provide for visual reasoning, we revisit it as negative-free stop-gradient alignment. This perspective shows that, although OPD provides effective token-level correction, its ceiling is constrained by the absence of trajectory-level discrimination. Motivated by these observations, we propose V-Zero, an answer-label-free framework for visual reasoning with contrastive evidence gating. V-Zero uses no annotated textual answer labels; instead, during training it pairs a question-relevant regional crop with a negative visual view to evaluate student-sampled trajectories and gate dense token-level distillation. Experiments on multiple visual reasoning benchmarks show that V-Zero consistently improves fine-grained visual reasoning while preserving strong generalization. Notably, V-Zero is more than 5times faster than previous supervised fine-tuning methods and more than 10times faster than reinforcement learning baselines. Code and dataset will be released at https://github.com/eVI-group-SCU/V-Zero