Key to Diffusion LMs Is Learning When to Commit to Tokens
furongh · x · 2026-07-06
Revealing an incorrect token too early locks in a bad constraint, while revealing highly dependent tokens in parallel can break consistency. Therefore, the real question isn't simply "can diffusion language models decode in parallel," but rather "can they learn when to commit?"
Related event: ICML 2026 Paper SAS: Optimizing Thought Scheduling in Diffusion LMs(15 posts)→
More from Research
- 3D ResNet Paper Crosses 3,000 Citations Eight Years After CVPR 2018 — HirokatuKataoka · 2026-09-11
- Sample selection and ordering matter a lot in LLM training: DataFlex makes data scheduling dynamic — Puzzleheaded_Box2842 · 2026-09-11
- Jeff Heaton's Intro to the Math of Neural Networks eBook Is Free to Download — blaizedsouza · 2026-09-11
- Mathematician Daniel Litt Launches Problem Repo to Track Human vs AI Progress: 15 Problems, 1 Solved — littmath · 2026-09-11
- Open ECDSA.fail challenge uses AI agents to shrink Shor's-algorithm quantum circuits for Bitcoin keys — StefanoGogioso · 2026-09-11
- Alex Townsend posts 200 open problems in numerical linear algebra for humans and AI agents — IgorCarron · 2026-09-11