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arxiv: 2605.05727 · v2 · pith:GKZ7IQYGnew · submitted 2026-05-07 · 💻 cs.DC

LLM-Enhanced Deep Reinforcement Learning for Task Offloading in Collaborative Edge Computing

Hao Guo , Kaixiang Xu , Ziwu Ge , Lei Yang This is my paper

Pith reviewed 2026-06-30 23:39 UTC · model grok-4.3

classification 💻 cs.DC
keywords task offloadingcollaborative edge computingdeep reinforcement learninglarge language modelshybrid decision frameworkself-attention moduletask success ratenode failures
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The pith

LeDRL pairs a lightweight LLM with self-attention DRL to raise task offloading success rates in collaborative edge networks by more than 17 percent.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces LeDRL as a hybrid method that feeds structured prompts describing node states, tasks, and links into a lightweight LLM to generate strategy guidance for a deep reinforcement learning policy. This guidance passes through a self-attention alignment step and receives refinement from a reflective evaluator that reviews past outcomes. The result is faster convergence and higher reliability when nodes fail unpredictably. A reader would care because offloading decisions directly control latency and completion rates in distributed edge systems. The authors validate the approach in simulations across network sizes and on physical Jetson hardware.

Core claim

LeDRL couples a lightweight LLM with self-attention-enhanced DRL for real-time task offloading. Structured prompts capture node status, task semantics, and link dynamics to produce high-level strategy priors. A self-attention alignment module processes these priors for context-aware policy optimization, while a reflective evaluator distills semantic feedback from trajectories to refine future prompts. Experiments across network scales show over 17 percent higher task success rates, quicker convergence, and improved real-time responsiveness versus representative baselines, with successful deployment on resource-limited Jetson edge devices via the CoEdgeSys prototype.

What carries the argument

The LeDRL hybrid framework that derives strategy priors from LLM-processed prompts, aligns them via self-attention, and refines them through reflective trajectory evaluation to steer the DRL policy.

If this is right

  • Task success rates rise by more than 17 percent relative to standard DRL and other baselines.
  • The combined system reaches stable policies in fewer training steps across varying network sizes.
  • Real-time responsiveness improves because high-level priors reduce the exploration burden on the DRL agent.
  • The approach remains practical on embedded hardware such as Jetson devices under tight compute limits.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The reflective evaluator could be extended to log decision rationales, making policies more inspectable in production edge deployments.
  • Similar prompt-and-evaluator patterns might transfer to other online optimization settings such as dynamic spectrum allocation or vehicle routing under uncertainty.
  • If the LLM priors prove robust, the method could lower overall energy use by cutting failed task executions that require retransmission.
  • Testing the framework with larger language models on more powerful edge nodes would reveal whether accuracy gains outweigh added latency.

Load-bearing premise

Structured prompts will let the lightweight LLM generate useful strategy guidance for the DRL without adding harmful uncertainty or unacceptable overhead.

What would settle it

A controlled experiment in which removing the LLM prior component causes the DRL policy to match or exceed LeDRL success rates in simulations with frequent node failures would falsify the claimed benefit.

Figures

Figures reproduced from arXiv: 2605.05727 by Hao Guo, kaixiang Xu, Lei Yang, Ziwu Ge.

Figure 1
Figure 1. Figure 1: Adaptive vs. static task offloading in a detection scenario. (a) Node 1 offloads to Node 4. (b) At t1, Node 3 fails—static strategy cannot adapt. (c) Adaptive method anticipates failure and reroutes tasks via alternate paths. (d) At t2, Node 7 joins; the adaptive strategy leverages its low load and proximity to improve latency. can anticipate such failures and reroute tasks through more stable paths ( view at source ↗
Figure 2
Figure 2. Figure 2: System overview. Tasks arrive at distributed edge nodes. Each node maintains local execution and communication queues, and a task can be processed locally or forwarded over multiple hops before execution at a destination node. We consider a collaborative edge system modeled as a time￾varying undirected graph G(t) = (V(t), E(t)), where V(t) and E(t) denote the active nodes and available bidirectional links … view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the LeDRL framework. An LLM provides semantic guidance during DRL decision-making. A self-attention fusion module merges LLM guidance with the DRL policy, and the RL agent outputs a hybrid offloading decision. A. Dec-POMDP Formulation To enable online offloading under stochastic arrivals and topology changes, we formulate the decision process as a Dec￾POMDP. At time t, choosing action a t i = j… view at source ↗
Figure 4
Figure 4. Figure 4: Learning curves of task success rates under different methods for different view at source ↗
Figure 5
Figure 5. Figure 5: Success rate of tasks under different:(a) size; (b) complexity; (c) execution failure rates; (d) transmission failure rates. view at source ↗
Figure 7
Figure 7. Figure 7: Internal architecture of the CoEdgeSys running on each edge device. view at source ↗
Figure 8
Figure 8. Figure 8: LeDRL success rate under different YOLO Confidence ( view at source ↗
read the original abstract

Collaborative edge computing uses edge nodes in different locations to execute tasks, necessitating dynamic task offloading decisions to maintain low latency and high reliability, especially under unpredictable node failures. Although deep reinforcement learning (DRL) and large language models (LLMs) have shown promise for task offloading, DRL often suffers from poor sample efficiency and local optima, while LLMs are difficult to use directly due to inference overhead and output uncertainty. To address these limitations, we propose \textbf{LeDRL}, a hybrid decision framework that couples a \emph{lightweight LLM} with self-attention-enhanced DRL for real-time task offloading. LeDRL constructs structured, context-aware prompts capturing node status, task semantics, and link dynamics to derive high-level strategy priors. These are selectively processed by a self-attention-based alignment module for context-aware policy optimization. A reflective evaluator further distills semantic feedback from past trajectories to refine subsequent prompts and provide consistent guidance. Extensive experiments show that LeDRL outperforms representative baselines in task success rate, convergence speed, and real-time responsiveness across diverse network scales, achieving over 17\% improvement in success rate. Furthermore, we deploy LeDRL on Jetson-based edge devices using our prototype system \textit{CoEdgeSys}, demonstrating its robustness and feasibility under resource constraints. Our code is available at:https://github.com/GalleyG5/LeDRL.git.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper presents LeDRL, a hybrid framework that couples a lightweight LLM with self-attention-enhanced DRL for dynamic task offloading in collaborative edge computing. Structured prompts encode node status, task semantics, and link dynamics to produce high-level strategy priors; these are processed by a self-attention alignment module and refined via a reflective evaluator that distills semantic feedback from trajectories. Experiments across network scales claim >17% higher task success rate, faster convergence, and improved real-time responsiveness versus representative baselines, with a prototype deployment on Jetson devices via CoEdgeSys; code is released at https://github.com/GalleyG5/LeDRL.git.

Significance. If the reported gains are shown to arise specifically from the LLM-generated priors rather than DRL architecture alone, the work would demonstrate a practical route to injecting semantic reasoning into real-time DRL policies for edge systems while respecting resource limits. The public code release is a clear strength that enables direct verification and extension.

major comments (3)
  1. [Abstract] Abstract: the claim of 'over 17% improvement in success rate' supplies no information on baseline definitions, number of runs, variance, or statistical tests, leaving it impossible to determine whether the hybrid LLM component drives the result.
  2. [Experiments] Experimental section: no ablation is described that disables only the lightweight LLM (retaining self-attention DRL, alignment module, and reflective evaluator); without this isolation the 17% gain cannot be attributed to the LLM priors rather than the DRL changes.
  3. [Deployment] Deployment section: the Jetson prototype reports neither the fraction of decisions that incorporate LLM output nor per-decision latency/variance statistics, so the claim of low-overhead real-time operation under the weakest assumption (useful priors without harmful uncertainty) remains unverified.
minor comments (1)
  1. The abstract refers to 'representative baselines' without naming them; listing the baselines would improve immediate clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important areas for clarifying the LLM's specific contribution and strengthening the experimental evidence. We address each major comment below and will revise the manuscript to incorporate additional details and analyses.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of 'over 17% improvement in success rate' supplies no information on baseline definitions, number of runs, variance, or statistical tests, leaving it impossible to determine whether the hybrid LLM component drives the result.

    Authors: We agree the abstract is too concise on these points. In the revision we will add a brief clause specifying the main baselines (standard DRL, heuristic offloading, and non-LLM variants), the number of independent runs (10), and that results include standard deviation with statistical significance (p < 0.05) reported in the experiments section. The 17 % figure is the aggregate improvement of the full LeDRL system; the experiments section already links the gains to the LLM priors via multiple comparisons, which we will cross-reference explicitly from the abstract. revision: yes

  2. Referee: [Experiments] Experimental section: no ablation is described that disables only the lightweight LLM (retaining self-attention DRL, alignment module, and reflective evaluator); without this isolation the 17% gain cannot be attributed to the LLM priors rather than the DRL changes.

    Authors: This observation is correct. The current manuscript compares LeDRL against external baselines but does not isolate the LLM priors while keeping the self-attention DRL, alignment module, and reflective evaluator intact. We will add a dedicated ablation subsection that reports performance of the self-attention DRL agent with and without the LLM-generated priors. The resulting delta will directly quantify the contribution of the lightweight LLM component. revision: yes

  3. Referee: [Deployment] Deployment section: the Jetson prototype reports neither the fraction of decisions that incorporate LLM output nor per-decision latency/variance statistics, so the claim of low-overhead real-time operation under the weakest assumption (useful priors without harmful uncertainty) remains unverified.

    Authors: We accept that the deployment section currently lacks these quantitative details. In the revised manuscript we will report the measured fraction of decisions that incorporate LLM priors (averaged over the prototype runs) together with per-decision latency statistics (mean, standard deviation, and 95th percentile) collected on the Jetson devices. These numbers will confirm that the overhead remains compatible with real-time constraints. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical performance claims rest on external experimental comparisons

full rationale

The paper describes a hybrid LLM-DRL framework (LeDRL) and reports empirical results from experiments and Jetson deployment showing >17% success-rate gains over baselines. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. The central claims are validated against independent baselines rather than reducing to inputs by construction, satisfying the self-contained criterion.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that an LLM can extract actionable strategy priors from network-state prompts and that these priors improve DRL sample efficiency without circular dependence on the final performance metric.

axioms (1)
  • domain assumption A lightweight LLM supplied with structured prompts on node status, task semantics, and link dynamics produces useful high-level strategy priors for DRL policy optimization.
    This premise is invoked to justify the hybrid architecture and is not derived from first principles within the work.

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Reference graph

Works this paper leans on

26 extracted references · 3 canonical work pages · 2 internal anchors

  1. [1]

    Decentralized task offloading in edge computing: an offline-to-online reinforcement learning approach.IEEE Transactions on Computers, 2024

    Hongcai Lin, Lei Yang, Hao Guo, and Jiannong Cao. Decentralized task offloading in edge computing: an offline-to-online reinforcement learning approach.IEEE Transactions on Computers, 2024

    2024

  2. [2]

    Online dis- tributed waveform-synchronization for acoustic sensor networks with dynamic topology.EURASIP Journal on Audio, Speech, and Music Processing, 2023(1):55, 2023

    Aleksej Chinaev, Niklas Knaepper, and Gerald Enzner. Online dis- tributed waveform-synchronization for acoustic sensor networks with dynamic topology.EURASIP Journal on Audio, Speech, and Music Processing, 2023(1):55, 2023

    2023

  3. [3]

    Definition of multi-objective deep reinforcement learning reward functions for self-driving vehicles in the urban environment

    Kaya Kuru. Definition of multi-objective deep reinforcement learning reward functions for self-driving vehicles in the urban environment. IEEE Transactions on Intelligent Transportation Systems, 2023

    2023

  4. [4]

    Imitation learning enabled fast and adaptive task scheduling in cloud.Future Generation Computer Systems, 154:160–172, 2024

    KaiXuan Kang, Ding Ding, HuaMao Xie, et al. Imitation learning enabled fast and adaptive task scheduling in cloud.Future Generation Computer Systems, 154:160–172, 2024

    2024

  5. [5]

    Qwen3 Technical Report

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report.arXiv preprint arXiv:2505.09388, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  6. [6]

    Task offloading with llm-enhanced multi-agent reinforcement learning in uav-assisted edge computing.Sensors, 25(1):175, 2024

    Feifan Zhu, Fei Huang, Yantao Yu, Guojin Liu, and Tiancong Huang. Task offloading with llm-enhanced multi-agent reinforcement learning in uav-assisted edge computing.Sensors, 25(1):175, 2024

    2024

  7. [7]

    Task offloading with large language models in mobile edge computing

    Youngjin Song, Wookjin Lee, and Sang Hyun Lee. Task offloading with large language models in mobile edge computing. In2024 15th International Conference on Information and Communication Technology Convergence (ICTC), pages 917–921. IEEE, 2024

    2024

  8. [8]

    Task offloading strategies for mobile edge computing: A survey.Computer Networks, page 110791, 2024

    Shi Dong, Junxiao Tang, Khushnood Abbas, Ruizhe Hou, Joarder Kam- ruzzaman, Leszek Rutkowski, and Rajkumar Buyya. Task offloading strategies for mobile edge computing: A survey.Computer Networks, page 110791, 2024

    2024

  9. [9]

    Dependent task offloading for edge computing based on deep reinforcement learning

    Jin Wang, Jia Hu, Geyong Min, and Wenhan Zhan. Dependent task offloading for edge computing based on deep reinforcement learning. IEEE Transactions on Computers, 71(10):2449–2461, 2021

    2021

  10. [10]

    Dependency tasks offloading and communication resource allocation in collaborative uav networks: A metaheuristic approach

    Loc X Nguyen, Yan Kyaw Tun, Tri Nguyen Dang, Yu Min Park, and Han. Dependency tasks offloading and communication resource allocation in collaborative uav networks: A metaheuristic approach. IEEE Internet of Things Journal, 10(10):9062–9076, 2023

    2023

  11. [11]

    Meson: A mobility-aware dependent task offloading scheme for urban vehicular edge computing.IEEE Transactions on Mobile Computing, 23(5):4259–4272, 2023

    Liang Zhao, Enchao Zhang, Shaohua Wan, Ammar Hawbani, Al-Dubai, et al. Meson: A mobility-aware dependent task offloading scheme for urban vehicular edge computing.IEEE Transactions on Mobile Computing, 23(5):4259–4272, 2023

    2023

  12. [12]

    Wirelessagent: Large language model agents for intelligent wireless networks.arXiv preprint arXiv:2505.01074, 2025

    Jingwen Tong, Wei Guo, Jiawei Shao, Qiong Wu, Zijian Li, Zehong Lin, and Jun Zhang. Wirelessagent: Large language model agents for intelligent wireless networks.arXiv preprint arXiv:2505.01074, 2025

    work page arXiv 2025

  13. [13]

    Resource allocation for stable llm training in mobile edge computing

    Chang Liu and Jun Zhao. Resource allocation for stable llm training in mobile edge computing. InProceedings of the Twenty-fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, pages 81–90, 2024

    2024

  14. [14]

    Industrial internet of things with large language models (llms): an intelligence-based reinforcement learning approach.IEEE Transactions on Mobile Computing, 2024

    Yuzheng Ren, Haijun Zhang, F Richard Yu, Wei Li, Pincan Zhao, and Ying He. Industrial internet of things with large language models (llms): an intelligence-based reinforcement learning approach.IEEE Transactions on Mobile Computing, 2024

    2024

  15. [15]

    Deep reinforcement learning for task offloading in mobile edge computing systems.IEEE Transactions on Mobile Computing, 21(6):1985–1997, 2020

    Ming Tang and Vincent WS Wong. Deep reinforcement learning for task offloading in mobile edge computing systems.IEEE Transactions on Mobile Computing, 21(6):1985–1997, 2020

    1985

  16. [16]

    Hybrid redundancy for reliable task offloading in collaborative edge computing

    Hao Guo, Lei Yang, Qingfeng Zhang, and Jiannong Cao. Hybrid redundancy for reliable task offloading in collaborative edge computing. IEEE Transactions on Computers, 2025

    2025

  17. [17]

    Ieee 802.11 mac- level fec scheme with retransmission combining.IEEE Transactions on Wireless Communications, 5(1):203–211, 2006

    Sunghyun Choi, Youngkyu Choi, and Inkyu Lee. Ieee 802.11 mac- level fec scheme with retransmission combining.IEEE Transactions on Wireless Communications, 5(1):203–211, 2006

    2006

  18. [18]

    Joint optimization of computing offloading and service caching in edge computing-based smart grid

    Huan Zhou and Zhenyu Zhang. Joint optimization of computing offloading and service caching in edge computing-based smart grid. IEEE Transactions on Cloud Computing, 11(2):1122–1132, 2022

    2022

  19. [19]

    The surprising effectiveness of ppo in cooperative multi-agent games.Advances in Neural Information Processing Systems, 35:24611–24624, 2022

    Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu. The surprising effectiveness of ppo in cooperative multi-agent games.Advances in Neural Information Processing Systems, 35:24611–24624, 2022

    2022

  20. [20]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin, Ming-Wei Chang, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. InProceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 4171–4186, 2019

    2019

  21. [21]

    The internet topology zoo.IEEE Journal on Selected Areas in Communications, 29(9):1765–1775, 2011

    Simon Knight, Hung X Nguyen, Nickolas Falkner, Rhys Bowden, and Matthew Roughan. The internet topology zoo.IEEE Journal on Selected Areas in Communications, 29(9):1765–1775, 2011

    2011

  22. [22]

    Value-Decomposition Networks For Cooperative Multi-Agent Learning

    Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czar- necki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, et al. Value- decomposition networks for cooperative multi-agent learning.arXiv preprint arXiv:1706.05296, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  23. [23]

    Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

    Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. InInternational conference on machine learning, pages 1861–1870. Pmlr, 2018

    2018

  24. [24]

    Power of random choices made efficient for fog computing.IEEE Transactions on Cloud Computing, 10(2):1130–1141, 2021

    Roberto Beraldi and Gabriele Proietti Mattia. Power of random choices made efficient for fog computing.IEEE Transactions on Cloud Computing, 10(2):1130–1141, 2021

    2021

  25. [25]

    Cost-efficient task offloading in mobile edge computing with layered unmanned aerial vehicles.IEEE Internet of Things Journal, 11(19):30496–30509, 2024

    Haitao Yuan, Meijia Wang, Bi, et al. Cost-efficient task offloading in mobile edge computing with layered unmanned aerial vehicles.IEEE Internet of Things Journal, 11(19):30496–30509, 2024

    2024

  26. [26]

    Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652, 2023

    Noah Shinn, Federico Cassano, et al. Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652, 2023

    2023