mlsanityData to 7 Oct 2026

Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

arXiv 2406.02616 ↗First posted 3 Jun 2024cs.LG cs.AIcited by 12

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Abstract (arXiv)

Optimizing the deployment of large language models (LLMs) in edge computing environments is critical for enhancing privacy and computational efficiency. Toward efficient wireless LLM inference in edge computing, this study comprehensively analyzes the impact of different splitting points in mainstream open-source LLMs. On this basis, this study introduces a framework taking inspiration from model-based reinforcement learning (MBRL) to determine the optimal splitting point across the edge and user equipment (UE). By incorporating a reward surrogate model, our approach significantly reduces the computational cost of frequent performance evaluations. Extensive simulations demonstrate that this method effectively balances inference performance and computational load under varying network conditions, providing a robust solution for LLM deployment in decentralized settings.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 36 of its 65 references that are in the map. First posted on arXiv 3 Jun 2024.

Builds on

36 of 65 references.

References · 36

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 5.

12 papers.

  1. 2025Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMscited by 12
  2. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
  3. 2025The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networkscited by 6
  4. 2025Malware Detection at the Edge with Lightweight LLMs: A Performance Evaluationcited by 4
  5. 2025Dual-Mind World Models: A General Framework for Learning in Dynamic Wireless Networkscited by 4
  6. 2025Intelligent Orchestration of Distributed Large Foundation Model Inference at the Edgecited by 1
  7. 2025Joint Partitioning and Placement of Foundation Models for Real-Time Edge AIcited by 1
  8. 2026Quantization-Aware Collaborative Inference for Large Embodied AI Modelscited by 1
  9. 2026Multi-Agentic AI for Fairness-Aware and Accelerated Multi-modal Large Model Inference in Real-world Mobile Edge Networkscited by 0
  10. 2026Privacy-Aware Split Inference with Speculative Decoding for Large Language Models over Wide-Area Networkscited by 0
  11. 2026Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel Statescited by 0
  12. 2026Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessingcited by 0