LEED: A Highly Efficient and Scalable LLM-Empowered Expert Demonstrations Framework for Multi-Agent Reinforcement Learning

arXiv 2509.14680 ↗First posted 18 Sep 2025cs.MA cs.LGcited by 6

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

Multi-agent reinforcement learning (MARL) holds substantial promise for intelligent decision-making in complex environments. However, it suffers from a coordination and scalability bottleneck as the number of agents increases. To address these issues, we propose the LLM-empowered expert demonstrations framework for multi-agent reinforcement learning (LEED). LEED consists of two components: a demonstration generation (DG) module and a policy optimization (PO) module. Specifically, the DG module leverages large language models to generate instructions for interacting with the environment, thereby producing high-quality demonstrations. The PO module adopts a decentralized training paradigm, where each agent utilizes the generated demonstrations to construct an expert policy loss, which is then integrated with its own policy loss. This enables each agent to effectively personalize and optimize its local policy based on both expert knowledge and individual experience. Experimental results show that LEED achieves superior sample efficiency, time efficiency, and robust scalability compared to state-of-the-art baselines.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 35 of its 45 references that are in the map. First posted on arXiv 18 Sep 2025.

Builds on

35 of 45 references.

References · 35

2017Proximal Policy Optimization Algorithmscited by 15,215
2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 567
2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agentscited by 398
2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 384
2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 294
2023Reward Design with Language Modelscited by 261
2021Trust Region Policy Optimisation in Multi-Agent Reinforcement Learningcited by 186
2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95

23 more

2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 90
2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
2024SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 62
2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
2019Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Criticscited by 47
2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
2021Multi-Agent Constrained Policy Optimisationcited by 35
2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 31
2024LLM-Empowered State Representation for Reinforcement Learningcited by 28
2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
2024IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengerscited by 20
2024MERIT: Multimodal Wearable Vital Sign Waveform Monitoringcited by 16
2025LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Datacited by 15
2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Surveycited by 14
2025Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoringcited by 10
2019Exploiting Fast Decaying and Locality in Multi-Agent MDP with Tree Dependence Structurecited by 8
2025Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attackscited by 7
2021Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communicationcited by 5
2025RRTO: A High-Performance Transparent Offloading System for Model Inference in Mobile Edge Computingcited by 5
2024ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clusteringcited by 1
2025Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computingcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

2026HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Expertscited by 11
2025LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systemscited by 8
2026Conflict-Aware Client Selection for Multi-Server Federated Learningcited by 5
2026SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compressioncited by 1
2026SIDeR: Semantic Identity Decoupling for Unrestricted Face Privacycited by 0
2026Large Language Model Enhanced Differentiable Trajectory Planning for IoT-Enabled Autonomous Drivingcited by 0

Similar, not cited

Close in wording, no citation either way