mlsanityData to 14 Oct 2026

QLLM: Do We Really Need a Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning?

arXiv 2504.12961 ↗First posted 17 Apr 2025cs.MA cs.AIcited by 6

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

Abstract (arXiv)

Credit assignment remains a fundamental challenge in multi agent reinforcement learning (MARL) and is commonly addressed through value decomposition under the centralized training with decentralized ex ecution (CTDE) paradigm. However, existing value decomposition meth ods typically rely on predefined mixing networks that require additional training, often leading to imprecise credit attribution and limited in terpretability. We propose QLLM, a novel framework that leverages large language models (LLMs) to construct training-free credit assign ment functions (TFCAFs), where the TFCAFs are nonlinear with re spect to the global state and offer enhanced interpretability while intro ducing no extra learnable parameters. A coder-evaluator framework is employed to ensure the correctness and executability of the generated code. Extensive experiments on standard MARL benchmarks demon strate that QLLM consistently outperforms baselines while requiring fewer learnable parameters. Furthermore, it demonstrates generalization across a broad set of value decomposition algorithms. Code is available at https://github.com/MaoMaoLYJ/pymarl-qllm.

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 42 of its 64 references that are in the map. First posted on arXiv 17 Apr 2025.

Builds on

42 of 64 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning19 Feb
  2. 2025Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning6 Feb
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learning20 Mar

Other references · 39

  1. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2019Language Models as Knowledge Bases?cited by 1,936
  4. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  5. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
  6. 2016HyperNetworkscited by 1,027
  7. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  8. 2017Counterfactual Multi-Agent Policy Gradientscited by 751
  9. 2024A Survey on Large Language Models for Code Generationcited by 658
  10. 2019The StarCraft Multi-Agent Challengecited by 597
  11. 2023Eureka: Human-Level Reward Design via Coding Large Language Modelscited by 571
  12. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
27 more
  1. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  2. 2019QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learningcited by 379
  3. 2022A Systematic Evaluation of Large Language Models of Codecited by 363
  4. 2020QPLEX: Duplex Dueling Multi-Agent Q-Learningcited by 270
  5. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 269
  6. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 261
  7. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 260
  8. 2022Pre-Trained Language Models for Interactive Decision-Makingcited by 221
  9. 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 212
  10. 2022Exploring Length Generalization in Large Language Modelscited by 206
  11. 2020Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 197
  12. 2019A Review of Cooperative Multi-Agent Deep Reinforcement Learningcited by 147
  13. 2020Explainable Reinforcement Learning: A Surveycited by 141
  14. 2020Qatten: A General Framework for Cooperative Multiagent Reinforcement Learningcited by 81
  15. 2021Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learningcited by 68
  16. 2020Value-Decomposition Multi-Agent Actor-Criticscited by 48
  17. 2023Towards LLM-guided Causal Explainability for Black-box Text Classifierscited by 44
  18. 2023Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approachcited by 38
  19. 2022MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffercited by 34
  20. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 32
  21. 2022Attention Mechanism in Neural Networks: Where it Comes and Where it Goescited by 29
  22. 2024Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learningcited by 23
  23. 2023State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understandingcited by 17
  24. 2022Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learningcited by 13
  25. 2023LanGWM: Language Grounded World Modelcited by 8
  26. 2021SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learningcited by 7
  27. 2024Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learningcited by 7

Built on by

Cited by year (2026 to 14 Oct)

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

6 papers.

  1. 2026From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Modelscited by 23
  2. 2026Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judgecited by 18
  3. 2025Maestro: Learning to Collaborate via Conditional Listwise Policy Optimization for Multi-Agent LLMscited by 14
  4. 2026Counterfactual Credit Policy Optimization for Multi-Agent Collaborationcited by 10
  5. 2026Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learningcited by 9
  6. 2026medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026LLM Value Functions for Credit Assignment in Agentic RL
  2. 2024VinePPO: Refining Credit Assignment in RL Training of LLMs
  3. 2026Agent Q-Mix: Selecting the Right Action for LLM Multi-Agent Systems through Reinforcement Learning
  4. 2024Mixed Q-Functionals: Advancing Value-Based Methods in Cooperative MARL with Continuous Action Domains
  5. 2025Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning
  6. 2025Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment
  7. 2026Explicit Credit Assignment through Local Rewards and Dependence Graphs in Multi-Agent Reinforcement Learning
  8. 2026QSIM: Mitigating Overestimation in Multi-Agent Reinforcement Learning via Action Similarity Weighted Q-Learning