arXiv AI June 29, 2026 By Yiheng Tao, Yihe Zhang, Matthew Dearing, Xin Wang, Yuping Fan, Michael E. Papka, Zhiling Lan

Ranking Before Serving: Low-Latency LLM Serving via Pairwise Learning-to-Rank

arXiv:2510. 03243v3 Announce Type: replace-cross Abstract: Efficient scheduling of large language model (LLM) inference tasks is critical for achieving low latency and high throughput, a challenge that is becoming increasingly acute with the rise of reasoning-capable LLMs whose generation lengths are highly variable.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 18

Beyond Prediction: Tail-Aware Scheduling for LLM Inference

arXiv:2606. 18431v1 Announce Type: new Abstract: LLM serving exhibits extreme length variability, making size-based scheduling difficult in practice.

By Yueying Li, Yuanfan Chen, Jiayang Chen, Esha Choukse, Haoran Qiu, G. Edward Suh, Rodrigo Fonseca, Ziv Scully, Udit Gupta

llms

arXiv AI

Jun 30

LLM Serving Optimization with Variable Prefill and Decode Lengths

arXiv:2508. 06133v4 Announce Type: replace-cross Abstract: We study offline scheduling for large language model (LLM) serving under a fixed KV-cache memory budget, where requests have heterogeneous prompt (prefill) and response (decode) lengths.

By Meixuan Wang, Yinyu Ye, Zijie Zhou

llms nlp

arXiv Machine Learning

Aug 18

Multi-Bin Batching for Increasing LLM Inference Throughput

arXiv:2412. 04504v2 Announce Type: replace-cross Abstract: As large language models (LLMs) grow in popularity for their diverse capabilities, improving the efficiency of their inference systems has become increasingly critical.

By Ozgur Guldogan, Jackson Kunde, Kangwook Lee, Ramtin Pedarsani

llms efficiency

arXiv Machine Learning

Aug 18

Beyond Binary Priorities: Multi-Tier SLA Scheduling for Large Language Model Serving

arXiv:2608. 16336v1 Announce Type: cross Abstract: Modern LLM serving deployments must simultaneously satisfy heterogeneous service-level objectives (SLOs) across a diverse population of user tiers, ranging from latency-critical API calls to background batch processing.

By Anders Vestrum, Arya Raeesi, Hanna Roed

llms

arXiv AI

Aug 18

When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction

arXiv:2608. 15592v1 Announce Type: new Abstract: Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades throughput.

By Feiyang Ren, Shengtao Wen, Lingbing Guo, Yu Tian, Yuanning Cui, Xiang Chen

llms reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Sep 14

AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training

AsyncFlow is an asynchronous streaming reinforcement learning framework designed to improve the post‑training phase of large language models. It introduces a distributed data storage and transfer module that enables panoramic data management and fine‑grained scheduling, allowing automated pipeline overlapping and dynamic load balancing. The framework also employs an asynchronous producer‑consumer workflow to reduce computational idleness by deferring parameter updates within staleness thresholds, and it is architecturally decoupled from training and inference engines, providing modular, customizable user interfaces. Experiments show an average throughput improvement of 1.59× over the state‑of‑the‑art baseline.

By Zhenyu Han, Ansheng You, Haibo Wang, Kui Luo, Guang Yang, Wenqi Shi, Menglong Chen, Sicheng Zhang, Zeshun Lan, Chunshi Deng, Huazhong Ji, Wenjie Liu, Yu Huang, Yixiang Zhang, Chenyi Pan, Jing Wang, Xin Huang, Chunsheng Li, Jianping Wu

llms reinforcement-learning efficiency benchmarks

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Beyond Prediction: Tail-Aware Scheduling for LLM Inference

LLM Serving Optimization with Variable Prefill and Decode Lengths

Multi-Bin Batching for Increasing LLM Inference Throughput

Beyond Binary Priorities: Multi-Tier SLA Scheduling for Large Language Model Serving

When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction

AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training