Emu3: Next-Token Prediction for Image and Video Generation

BAAI's Emu3 (Wang et al., September 2024) is the 2024 result that should have ended the diffusion-versus-autoregressive debate. A single Llama-style decoder-only transformer, trained only on the next-token-prediction objective, across a unified vocabulary of text + VQ image tokens + 3D VQ video tokens, beats SDXL on image generation and LLaVA-1.6 on perception. No CLIP loss. No diffusion schedule. Classifier-free guidance is used at inference for quality, but the core training objective is next-token prediction with teacher forcing. Published in Nature. This lesson reads the Emu3 thesis — why a better tokenizer plus scale is all you need — and contrasts with diffusion approaches.