PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
June 17, 2026MathematicsOpen Access

SW-SpeedDLM: Sliding Window Speculative Decoding for Diffusion Language Models Under Long Context Constraints

View Full Paper
Ask AI
Bookmark
Share

Authors

TDTeng DaiMRMinjae RheeYQYuxuan Qin

Discussion

Loading...

Member takes

Overview

Randomized trial shows improved efficiency of long-context generation in diffusion language models, suggesting advances in GPU memory use.

Key Points

  • The aim is to enhance the efficiency of long-context generation using masked diffusion language models without exceeding GPU memory limits.
  • Introduced SW-SpeedDLM, an inference wrapper for MDLMs with three components.
  • Used Segmented Sliding Window Denoising to limit each denoising step to a W token window.
  • Implemented Cross-Segment KV Compression and Window Level Speculative Acceptance for increased decoding speed.
  • Achieved 3.7× higher throughput at n=8192 compared to full attention at n=2048.
  • Reduced peak memory usage by 2.5× compared to full attention at n=4096.
  • Increased PG-19 bits per character by only 0.18 with improved performance.

Cite This Study

Dai et al. (2026) studied this question.

synapsesocial.com/papers/6a323e9ed50b63ecad207c73https://doi.org/10.3390/math14122137
View Full Paper
Ask AI
Bookmark
Share