Unified multimodal models use a shared Transformer backbone for visual understanding and image generation. Existing post-training signals primarily constrain final outputs, output-side representations, or task-level behavior, providing limited supervision for how visual semantics are organized across intermediate layers. We propose HiSVR, a hierarchical semantic visual reconstruction method for post-training unified multimodal models. A frozen semantic visual tokenizer extracts residual semantic codes from each training image, which supervise image-associated hidden states at different Transformer depths. Shallow layers predict shorter semantic-code prefixes, while deeper layers reconstruct longer prefixes, yielding coarse-to-fine supervision over intermediate representations. To balance the differing optimization difficulty across depths and prefix lengths, we introduce an uncertainty-weighted progressive reconstruction loss. HiSVR is used only during training and removed at inference, preserving the original architecture and inference cost. Experiments on unified multimodal backbones show that HiSVR improves visual understanding while maintaining image generation quality. These results indicate that hierarchical semantic reconstruction effectively regularizes intermediate representations in unified multimodal models.