Post-training in diffusion models is a very under-appreciated topic. So, we're delighted to try to change that at ECCV'26. Announcing a dedicated tutorial for it with the best pack 🔥 We'll cover several tracks & check out the link below to know more! Hila Chefer, Linoy Tsaban
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Post-training for diffusion models feels under-discussed compared to LLMs. This is a useful direction, especially as image and video models move closer to real product workflows.
Post-training for diffusion models definitely deserves more attention Sounds like an exciting tutorial lineup for ECCV’26
Great work!!
🌲🌲🌲
Post-training being under-appreciated tracks, most of the public attention still goes to base architectures while the preference tuning is what actually shapes what ships. The harder problem you'll probably hit is evaluation: image preference is fuzzier than text, and reward hacking tends to show up as aesthetic mode collapse where everything drifts toward the same overcooked look. Curious whether the tutorial gets into how to catch that before it bakes into the model.
Tutorial: https://pdm-tut.github.io/