AI答非所“看”:给大模型发“分项成绩单”
现在的视觉AI模型能说会道，却常常“睁眼说瞎话”:它可能没看清图里的物体，就顺着话头编一串。研究者发现，问题出在训练时只给“对或错”一个总分，模型不知道自己错在哪儿。他们发明了一套“分项评分表”:把标准答案拆成一条条原子命题，分别检查模型是否看对了视觉事实、推理是否前后一致、有没有遵守指令，再把错题定位到具体步骤，让模型知道自己该改哪一步。在8B参数模型上，这套方法比只看总分的训练方式提升明显，尤其在需要“看图说话”的知识推理上。它不是能让你明天装上的插件，但指了一个方向：要让AI“诚实看图”，得先把“对”拆成能纠正的一个个小目标。
📄 原文摘要(英文)
Vision-language models can produce fluent answers that are insufficiently grounded in the visual evidence: a single unsupported object, chart value, or intermediate inference can undermine an otherwise plausible response. We argue that this is a credit-assignment failure in multimodal post-training. Scalar outcome rewards indicate whether an answer is acceptable, but do not identify which visual facts are grounded, which reasoning steps are valid, or which instruction constraints are missed. We introduce Visual Rubrics-Based Reinforcement Learning, which decomposes reference responses into atomic propositions and scores generated answers along Visual Faithfulness (VF), Reasoning Consistency (RC), and Instruction Following (IF). The resulting rubric items provide structured partial credit and localize rubric credit when supporting evidence spans are available. We first obtain an SFT checkpoint by fine-tuning Qwen3-VL-8B-Instruct on the public OpenMMReasoner-SFT-874K corpus, adapting OpenMMReasoner's cold-start data recipe. We construct V-Rubrics 50K, a 50,248-example training set from 17 visually grounded sources, by applying rule-based filters before deriving example difficulty from rejection-sampling scores and then annotating every example with Gemini-3-Pro under the same structured prompt and protocol. We train our model based on the same SFT checkpoint using component-wise, prefix-localized rubric credit. Experiments show that our rubricbased GRPO improves over both the shared SFT baseline and answer-only GRPO, with the largest gains on knowledge-oriented and visually grounded reasoning benchmarks. The results show rubrics as a useful reward abstraction for visual post-training.