Vision-language models align frozen vision encoders to language models using only a next-token prediction loss, an asymmetric signal that discards rich visual signals not captured in the captions. We propose GenDA (Generative Distillation Alignment), a novel training framework that augments the captioning loss with a generative distillation objective from a frozen text-to-image diffusion model. GenDA concatenates selected visual tokens with the encoded caption to form a joint conditioning signal, encouraging these tokens to encode visual information not already captured by the caption. Across five vision encoders and four language models, GenDA gains 3-9 CIDEr points over LLaVA-1.5 on five captioning benchmarks. These gains also transfer to downstream VLM benchmarks while preserving the encoders’ discriminative performance. Visual preservation and modality-gap analyses suggest that visual information is retained deeper within the LLM, while visual features become more closely aligned with the language representation space.