Unified Latents (UL): How to train your latents
Feb 28, 2026
On the February 19, 2026 paper Google Deepmind introduces Unified Latents (UL), a novel framework for generative modeling that jointly trains an encoder, a diffusion prior, and a diffusion decoder. By incorporating a fixed amount of Gaussian noise during the encoding process, the method creates a stable and interpretable bound on latent information. This architecture allows for precise control over the reconstruction-modeling tradeoff through simple hyperparameters like the loss factor and sigmoid weighting. Experimental results demonstrate that this approach is more computationally efficient than existing methods, achieving superior image and video generation quality on benchmarks like ImageNet and Kinetics-600. Ultimately, the research offers a principled alternative to traditional Variational AutoEncoders by simplifying the training objective and preventing issues like posterior collapse. Source: February 19, 2026 Unified Latents (UL): How to train your latents Google DeepMind Jonathan Heek, Emiel Hoogeboom, Thomas Mensink, Tim Salimans https://arxiv.org/pdf/2602.17270