How to Train Your Latent Diffusion Language Model Jointly With the Latent Space
Latent diffusion models offer an attractive alternative to discrete diffusion for non-autoregressive text generation by operating on continuous text representations and denoising entire sequences in parallel. The major challenge in latent diffusion modeling is constructing a suitable latent space. In this work, we present the Latent Diffusion Language Model (LDLM), in which the latent encoder, diffusion model, and decoder are trained jointly. LDLM builds its latent space by reshaping the representations of a pre-trained language model with a trainable encoder, yielding latents that are easy to both denoise and decode into tokens. We show that naive joint training produces a low-quality di...
| Authors | Viacheslav Meshchaninov, Alexander Shabalin, Egor Chimbulatov et al. |
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| Primary category | cs.CL |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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