AI RESEARCH
JEDI: Joint Embedding Diffusion World Model for Online Model-Based Reinforcement Learning
arXiv CS.LG
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ArXi:2605.13013v1 Announce Type: new Diffusion world models have recently become competitive for online model-based reinforcement learning, but current approaches expose a tension: pixel diffusion is effective but computationally expensive while the latest latent diffusion approach improves efficiency yet performs subpar. The latter also relies on separately trained latents rather than the end-to-end world-model objectives that have driven much of modern MBRL progress.