arXiv AI August 13, 2026 By Yifan Wang, Patrick Royer, Rapha\"el F\'eraud, David Delande

Reinforcement Learning based DBMS Buffer Pool Auto-Tuning for Optimal Memory Utilization

arXiv:2608. 11239v1 Announce Type: cross Abstract: Administering Database Management Systems (DBMS) instances requires Database Administrators (DBA) to balance performance in terms of Service Level Agreement (SLA) against resource usage, often prompting RAM over-allocation that wastes memory.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 10

HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning

arXiv:2601. 22448v2 Announce Type: replace Abstract: RLVR has become a standard recipe for training LLMs on reasoning tasks with verifiable outcomes, but when rollout generation dominates the cost, efficiency hinges on which prompts are sampled and when.

By Weiqi Wang, Xin Liu, Binxuan Huang, Hejie Cui, Rongzhi Zhang, Changlong Yu, Shuowei Jin, Jingfeng Yang, Qingyu Yin, Zhengyang Wang, Zheng Li, Yifan Gao, Priyanka Nigam, Bing Yin, Lihong Li, Yangqiu Song

llms reinforcement-learning benchmarks

arXiv Machine Learning

Aug 12

TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling

arXiv:2608. 10402v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with growing contexts, and finish at highly variable times.

By Yanyu Ren, Xizheng Wang, Xiao Liu, Bowen Lv, Hanchen Zhang, Shudan Zhang, Hanyu Lai, Shuai Wang, Li Chen, Dan Li, Jie Tang

llms agents reinforcement-learning efficiency multimodal

arXiv Machine Learning

Aug 10

Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving

arXiv:2608. 06557v1 Announce Type: cross Abstract: The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges to long, compute-heavy requests.

By Muhammad Adnan, Rohan Mahapatra, Prashant J. Nair, Daniel Berger, Pantea Zardoshti, Rodrigo Fonseca, Esha Choukse

llms agents safety

arXiv AI

Jul 7

Adaptive Inference Batching using Policy Gradients

arXiv:2607. 05272v1 Announce Type: cross Abstract: Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic.

By Ruslan Sharifullin

agents reinforcement-learning efficiency

arXiv Machine Learning

Aug 7

Hybrid-Adaptive Thread Tuning to Mitigate Simulation Execution Bottlenecks in High-Performance Reinforcement Learning Inference

arXiv:2608. 06025v1 Announce Type: new Abstract: In simulation-in-the-loop decision-making systems, reinforcement learning (RL) inference is often constrained by simulator-side execution overhead, where workloads are highly dynamic and sensitive to runtime thread configurations.

By Jiming Su, Hantao Hua, Lujia Yin, Yiping Yao, Feng Zhu

reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jun 29

End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference

arXiv:2606. 27743v1 Announce Type: cross Abstract: Large Language Models (LLMs) inference is typically deployed under a static resource assumption, where models execute a fixed computational graph regardless of the runtime environment.

By Yuhang Chen, Jinhao Duan, Ruichen Zhang, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Parish Aggarwal, Frank Shyu, Luke Simon, Sandeep Pandey, Tianlong Chen, Xi Liu

llms efficiency

Related stories

HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning

TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling

Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving

Adaptive Inference Batching using Policy Gradients

Hybrid-Adaptive Thread Tuning to Mitigate Simulation Execution Bottlenecks in High-Performance Reinforcement Learning Inference

End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference