Paper

Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers

Unreadunread

AI summary

arXiv:2605. 16941v1 Announce Type: new Abstract: Diffusion Large Language Models (DLLMs) promise fast parallel generation, yet open-source DLLMs still face a severe quality-speed trade-off: accelerating decoding by revealing multiple tokens often causes substantial quality degradation.

Reading status

Priority

About this paper

Authors: Fanqin Zeng, Feng Hong, Geng Yu, Huangjie Zheng, Xiaofeng Cao, Ya Zhang, Bo Han, Yanfeng Wang, Jiangchao Yao
Released: 2026-05-19 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, source, token, rate, language, model. Source: arxiv cs.CL (NLP).
arXiv: 2605.16941

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.