SAS Makes Schedulers Self-Aware
furongh · x · 2026-07-06
The core idea is to give the scheduler "self-awareness": SAS leverages the frozen model's own path likelihood to reward a specific unmasking sequence—essentially asking, "How well can I explain the target along this path?". This allows the model to learn a decoding order that naturally aligns with its own predictive strengths.
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