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.
More from Research
- EdgeRock open-sourced: 72 MiB KV cache at 256K context, 99.1% smaller than Qwen3.5-4B — alifcoder · 2026-10-27
- EdgeRock-4B holds KV cache at 72 MiB across 256K context, 99.1% smaller than Qwen3.5-4B — alifcoder · 2026-10-27
- TRL adds Score Centering, a simple fix for RLHF training instability — lvwerra · 2026-10-27
- Latent-Foresight: world models that learn to predict their own future — abursuc · 2026-10-27
- UK AISI papers: no good safeguards yet against misuse of biological AI tools — davidmanheim · 2026-10-27
- CMU's XYBench: LLMs answer queries literally and reinforce user misconceptions — valentina__py · 2026-10-27