RL-Guided Diffusion Models for New Material Discovery
bravo_abad · x · 2026-07-07
A detailed technical post discussed "reward-guided diffusion": using reinforcement learning as a post-training method to guide diffusion models out of their training distribution and sample novel structures from low-probability tails. Authors Hyunsoo Park and Aron Walsh reframed this as a post-training problem, noting that maximum likelihood diffusion models tend to reproduce familiar structures. However, valuable novel compounds for materials discovery lie in under-explored, low-probability regions, requiring active RL guidance to generate stable and novel crystals.
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