How to Train Diffusion LLM more efficiently? Our paper has an answer for you: Don’t Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment Diffusion language models are becoming increasingly attractive: they support bidirectional generation, non-sequential decoding, and flexible editing. But training them from scratch is expensive. So a natural question is: If we already have strong pretrained autoregressive LMs, do we really need to relearn all language representations for diffusion LMs? We argue: probably not. Our view is that AR→DLM conversion should not be treated as learning language from scratch again. Much of the semantic structure is already inside the AR model. What changes is the generation order and denoising behavior. So instead of only continuing denoising training, we explicitly preserve the representation geometry of the AR model. We introduce REPR-ALIGN: during masked diffusion training, we align the hidden states of the DLM to a frozen AR teacher of the same architecture, layer by layer, using cosine similarity. No adapters. No architectural changes beyond the attention mask. Just representation alignment + masked denoising. The result: up to 4× training acceleration in our setting, with especially strong gains in low-data regimes. The main takeaway is simple: Don’t retrain the representation space from scratch. Align it, and let the model relearn the decoding path. Paper: arxiv.org/abs/2605.06885 Code: github.com/pengzhangzhi/Open… Work done with an amazing undergrad @alexisfox and advisors @Anru_Zhang @AlexanderTong7