On Asymmetric Optimization of Reasoning and Perception in Vision-Language Model Post-Training 事件

On Asymmetric Optimization of Reasoning and Perception in Vision-Language Model Post-Training arXiv:2605.29496v1 Announce Type: cross Abstract: Post-training has greatly improved reasoning in frontier vision-language models, yet its gains for perception remain comparatively limited, creating a bottleneck for end-to-end visual reasoning. To investigate this gap, we introduce a controlled diagnostic framework with two synthetic tasks that disentangle perception from reasoning. Our analysis reveal