Key papers

• STEEL: Sparsity-Aware Fused Attention for Energy-Efficient Long-Sequence Inference on AMD's XDNA NPU (2026)
  Victor J. B. Jung et al.
  5.01
• Think Before You Grid-Search: Floor-First Triage for LLM Serving (2026)
  Yihua Liu
  4.39
• The Caf\'e in Amsterdam: When the Incumbent Becomes the Oracle (2026)
  Augusto Camargo
  4.39
• Accelerating Disaggregated RL for Visual Generative LLMs with Diffusion-Based Parallelism and Trainer-Assisted Generation (2026)
  Sijie Wang et al.
  4.33
• Kevin: Multi-Turn RL for Generating CUDA Kernels (2025)
  Carlo Baronio et al.
  2.93
• Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles (2025)
  Matteo Gallici et al.
  2.82
• Tabular and Deep Reinforcement Learning for Gittins Index (2024)
  Harshit Dhankhar and Kshitij Mishra and Tejas Bodas
  2.21
• A High-Throughput Compute-Efficient POMDP Hide-And-Seek-Engine (HASE) for Multi-Agent Operations (2026)
  Timothy Flavin et al.
  1.83
• Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control (2026)
  Nicolas Helson et al.
  1.78
• EARL: Energy-Aware Optimization of Liquid State Machines for Pervasive AI (2026)
  Zain Iqbal et al.
  1.67
• Offline Reinforcement-Learning-Based Power Control for Application-Agnostic Energy Efficiency (2026)
  Akhilesh Raj et al.
  1.67
• AutoGuard: A Self-Healing Proactive Security Layer for DevSecOps Pipelines Using Reinforcement Learning (2025)
  Praveen Anugula et al.
  1.61
• Prioritizing Latency with Profit: A DRL-Based Admission Control for 5G Network Slices (2025)
  Proggya Chakraborty et al.
  1.50
• Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks (2025)
  Jason Gardner et al.
  1.44
• Coordinated Reinforcement Learning Prefetching Architecture for Multicore Systems (2025)
  Mohammed Humaid Siddiqui et al.
  1.44
• Efficient Solving of Large Single Input Superstate Decomposable Markovian Decision Process (2025)
  Youssef Ait El Mahjoub et al.
  1.39
• BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search (2025)
  Azhar Ikhtiarudin et al.
  1.33
• A Comparative Study of OpenMP Scheduling Algorithm Selection Strategies (2025)
  Jonas H. M\"uller Kornd\"orfer et al.
  1.33
• Offline Reinforcement Learning for Mobility Robustness Optimization (2025)
  Pegah Alizadeh et al.
  1.28
• FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization system (2024)
  Zeyuan Li et al.
  0.89
• Dynamic Control Flow in Large-Scale Machine Learning (2018)
  Yuan Yu et al.
  —
• UNAS: Differentiable Architecture Search Meets Reinforcement Learning (2019)
  Arash Vahdat et al.
  —
• Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging (2020)
  Shigang Li et al.
  —
• Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations (2020)
  Yongchao Liu et al.
  —
• The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems (2020)
  Ahmet Inci et al.
  —
• Mind Mappings: Enabling Efficient Algorithm-Accelerator Mapping Space Search (2021)
  Kartik Hegde et al.
  —
• Optimal Path Planning of Autonomous Marine Vehicles in Stochastic Dynamic Ocean Flows using a GPU-Accelerated Algorithm (2021)
  Rohit Chowdhury et al.
  —
• Accelerating Distributed Deep Reinforcement Learning by In-Network Experience Sampling (2021)
  Masaki Furukawa et al.
  —
• Accelerating Deep Learning with Dynamic Data Pruning (2021)
  Ravi S Raju et al.
  —
• EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine (2022)
  Jiayi Weng et al.
  —
• Optimizing Data Collection in Deep Reinforcement Learning (2022)
  James Gleeson et al.
  —
• Associative Memory Based Experience Replay for Deep Reinforcement Learning (2022)
  Mengyuan Li et al.
  —
• POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning (2022)
  Shalini Jain et al.
  —
• Improving Operational Efficiency In EV Ridepooling Fleets By Predictive Exploitation of Idle Times (2022)
  Jesper C. Provoost et al.
  —
• FLAC: A Robust Failure-Aware Atomic Commit Protocol for Distributed Transactions (2023)
  Hexiang Pan et al.
  —
• Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions (2023)
  Tongxin Li et al.
  —
• InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models (2023)
  Kabir Nagrecha et al.
  —
• Context-Aware Composition of Agent Policies by Markov Decision Process Entity Embeddings and Agent Ensembles (2023)
  Nicole Merkle et al.
  —
• QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing (2023)
  Iman Rahmaty et al.
  —
• Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability (2023)
  C\'eline Comte et al.
  —
• iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning (2023)
  Xenofon Vasilakos and Shadi Moazzeni and Anderson Bravalheri and Pratchaya Jaisudthi and Reza Nejabati and Dimitra Simeonidou
  —
• Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach (2023)
  Sepide Saeedi et al.
  —
• Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems (2024)
  Neharika Jali et al.
  —
• Stochastic Q-learning for Large Discrete Action Spaces (2024)
  Fares Fourati et al.
  —
• GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS (2024)
  Saman Kazemkhani et al.
  —
• Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks (2024)
  Yan Dai and Longbo Huang
  —
• AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms (2024)
  Biman Barua and M. Shamim Kaiser
  —