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Lin Qu

14 papers hereh-index 7164 citations23 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author12
  • last author2

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.DC7
  • cs.LG3
  • cs.CV2
  • cs.CL1
  • cs.SE1
same name
  • Lin Qu — 23 papers, h 8
  • Lin Qu — 14 papers, h 3
  • Lin Qu — 6 papers, h 1
  • Lin Qu — 6 papers, h 2
  • Lin Qu — 4 papers, h 1
  • Lin Qu — 4 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedFALCON: Pinpointing and Mitigating Stragglers for Large-Scale Hybrid-Parallel Training

2 citations · 3 across the 14 of their papers we have counts for

collaborators
Showing 2025 · cs.DCShow all

4 papers · 2 filters

cs.DC2025

RollMux: Phase-Level Multiplexing for Disaggregated RL Post-Training

Tianyuan Wu, Lunxi Cao, Yining Wei +11

Rollout-training disaggregation is emerging as the standard architecture for Reinforcement Learning (RL) post-training, where memory-bound rollout and compute-bound training are ph…

cs.DC2025

RollArt: Disaggregated Multi-Task Agentic RL Training at Scale

Wei Gao, Yuheng Zhao, Tianyuan Wu +15

Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU…

cs.DC2025

InstGenIE: Generative Image Editing Made Efficient with Mask-aware Caching and Scheduling

Xiaoxiao Jiang, Suyi Li, Lingyun Yang +12

Generative image editing using diffusion models has become a prevalent application in today's AI cloud services. In production environments, image editing typically involves a mask…

cs.DC2025

Adaptra: Straggler-Resilient Hybrid-Parallel Training with Pipeline Adaptation

Tianyuan Wu, Lunxi Cao, Hanfeng Lu +8

Training large Deep Neural Network (DNN) models at scale often encounters straggler issues, mostly in communications due to network congestion, RNIC/switch defects, or topological…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.