dOPSD: Online Self-Distillation for Enhanced Diffusion LLM Inference
Phuong Tuan Dat · hf · 2026-07-07
Diffusion LLMs face unique challenges in enhancing reasoning capabilities through post-training. Researchers propose an online policy self-distillation method (dOPSD) that utilizes the model's internal denoising trajectories as training signals for self-distillation, eliminating the need for an external teacher model. Experiments show that this approach significantly improves the performance of diffusion language models in mathematical reasoning and code generation tasks.
Related event: dOPSD: A New Self-Distillation Paradigm for Diffusion LLMs(2 posts)→
More from Models
- A model benchmark shows Muse Spark far ahead of Grok-4.20 on score vs cost — cis_female · 2026-07-21
- A 600k-token relationship test compares how models comment on personal context — cis_female · 2026-07-21
- Cola launches July, the latest model jokingly billed as “second only to Fable” — oran_ge · 2026-07-21
- Kimi K3 looks stronger and about 5× cheaper on a frontend dashboard task — OwariDa · 2026-07-21
- Last Week in AI recap: Anthropic’s $65B round, IPO filing, and Microsoft’s MAI push — Last Week in AI · 2026-07-21
- A user says Claude 4.6 felt worse yesterday and asks whether model quality can drift over time — Rahios · 2026-07-21