Most latent generative models still follow the same two-stage recipe: train a VAE for reconstruction, freeze it, then force the generative model to learn whatever latent space it gets. But reconstruction-optimal latents are not necessarily generation-friendly — so why not let the generative model shape the latent space directly? ByteDance Seed presents GenFirst to make stable end-to-end latent learning work. This work revisit the VAE objective and identify a prior–entropy imbalance as the key cause of latent collapse in naive end-to-end training. After fixing collapse with explicit entropy preservation, they find that generation and reconstruction have strongly asymmetric learning dynamics. This leads to Generation Before Reconstruction (GenFirst): first let the generative objective shape a generation-friendly latent space, then strengthen reconstruction to recover visual details. The results are strong. With end-to-end SiT + VAE training, GenFirst achieves 0.97 gFID with CFG / 1.45 without CFG on ImageNet-256, and reaches the final SiT result with at least 70× fewer training steps. This is the first diffusion model to reach FID < 1.0 without an FD loss. On text-to-image generation, it reaches 0.90 GenEval. More importantly, this end-to-end framework goes beyond image generation: it learn a shared visual latent space that supports generation, representation learning, and reconstruction, and further extend end-to-end latent learning to continuous text latents, enabling unified text-to-image and image-to-text diffusion modeling. GenFirst: Generation Before Reconstruction for Stable End-to-End Latent Generative Modeling Paper: arxiv.org/abs/2608.29335 Project: tom-zgt.github.io/GenFirst/ HF Daily Paper: huggingface.co/papers/2608.2…