这篇论文提出了ViGOS，专门解决多模态模型自蒸馏时只看文本不看图的毛病，在多个视觉语言基准上有效果。
ViGOS是一种针对多模态大语言模型(MLLM)的后训练框架，采用解耦感知与推理的自蒸馏方法(OPSD)。学生模型先生成视觉描述，再基于此进行推理，避免直接利用文本目标产生捷径。实验在通用视觉-语言、专家推理、视觉数学、空间定位和视觉-语言先验五类基准上验证，ViGOS在易出现捷径的场景中显著提升了图像依赖行为。
Seeing Before Reasoning: Decoupling Perception and Reasoning for Shortcut-Resilient Multimodal On-Policy Self-Distillation
On-policy self-distillation (OPSD) trains a model on its own rollouts and uses a frozen copy to provide dense token-level targets conditioned on a reference target. This works well for LLM reasoning, but a direct extension to multimodal large language models (MLLMs) can create a shortcut: the privileged target may guide tokens mainly based on the text reference target rather than the image. We propose ViGOS, a visually grounded OPSD framework for MLLM post-training. The student first writes a visual description and then reasons toward the final answer. For valid rollouts, an image-only perception teacher supervises the description, while a privileged reasoning teacher supervises the reasoning and final answer on the same student prefix. A reference teacher is used only for invalid rollouts to recover the output format. Across general vision-language, expert reasoning, visual math, spatial grounding, and visual-language-prior benchmarks, ViGOS keeps the main benefits of OPSD and improves image-grounded behavior in shortcut-prone settings.