mlsanityData to 14 Oct 2026

Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning

arXiv 2502.03723 ↗First posted 6 Feb 2025cs.MAcited by 10

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

Abstract (arXiv)

Credit assignment, the process of attributing credit or blame to individual agents for their contributions to a team's success or failure, remains a fundamental challenge in multi-agent reinforcement learning (MARL), particularly in environments with sparse rewards. Commonly-used approaches such as value decomposition often lead to suboptimal policies in these settings, and designing dense reward functions that align with human intuition can be complex and labor-intensive. In this work, we propose a novel framework where a large language model (LLM) generates dense, agent-specific rewards based on a natural language description of the task and the overall team goal. By learning a potential-based reward function over multiple queries, our method reduces the impact of ranking errors while allowing the LLM to evaluate each agent's contribution to the overall task. Through extensive experiments, we demonstrate that our approach achieves faster convergence and higher policy returns compared to state-of-the-art MARL baselines.

Key facts. Cited by 10 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 26 of its 37 references that are in the map. First posted on arXiv 6 Feb 2025.

Builds on

26 of 37 references.

References · 26

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  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2017Deep reinforcement learning from human preferencescited by 3,912
  5. 2019Fine-Tuning Language Models from Human Preferencescited by 2,150
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  11. 2018Scalable agent alignment via reward modeling: a research directioncited by 447
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14 more
  1. 2018Reward learning from human preferences and demonstrations in Ataricited by 316
  2. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 311
  3. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingcited by 302
  4. 2020QPLEX: Duplex Dueling Multi-Agent Q-Learningcited by 270
  5. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 220
  6. 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 164
  7. 2022Multi-Agent Reinforcement Learning is a Sequence Modeling Problemcited by 155
  8. 2024A Minimaximalist Approach to Reinforcement Learning from Human Feedbackcited by 144
  9. 2021B-Pref: Benchmarking Preference-Based Reinforcement Learningcited by 106
  10. 2021Reward (Mis)design for Autonomous Drivingcited by 104
  11. 2020Value-Decomposition Multi-Agent Actor-Criticscited by 48
  12. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 32
  13. 2024Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Modelscited by 7
  14. 2023A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learningcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 3.

10 papers.

  1. 2026Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judgecited by 18
  2. 2025Maestro: Learning to Collaborate via Conditional Listwise Policy Optimization for Multi-Agent LLMscited by 14
  3. 2025Advancing Language Multi-Agent Learning with Credit Re-Assignment for Interactive Environment Generalizationcited by 11
  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. 2025QLLM: Do We Really Need a Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning?cited by 6
  7. 2025Multi-Stakeholder Alignment in LLM-Powered Collaborative AI Systems: A Multi-Agent Framework for Intelligent Tutoringcited by 2
  8. 2025ROAD: Responsibility-Oriented Reward Design for Reinforcement Learning in Autonomous Drivingcited by 0
  9. 2025Agent2: An Agent-Generates-Agent Framework for Reinforcement Learning Automationcited by 0
  10. 2025Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment
  2. 2024Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning
  3. 2026From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models
  4. 2026LLM Value Functions for Credit Assignment in Agentic RL
  5. 2026Unifying Temporal and Structural Credit Assignment in LLM-Based Multi-Agent Prompt Optimization
  6. 2026LangMARL: Natural Language Multi-Agent Reinforcement Learning
  7. 2026The End of Reward Engineering: How LLMs Are Redefining Multi-Agent Coordination
  8. 2024Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL