@inproceedings{yao-etal-2026-langmarl,
title = "{L}ang{MARL}: Natural Language Multi-Agent Reinforcement Learning via Semantic Credit Assignment",
author = "Yao, Huaiyuan and
Da, Longchao and
Liu, Xiaoou and
Fleming, Charles and
Chen, Tianlong and
Wei, Hua",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1837/",
pages = "41793--41807",
ISBN = "979-8-89176-459-0",
abstract = "Large language model (LLM) agents struggle to autonomously evolve coordination strategies in dynamic environments, largely because coarse global outcomes obscure the causal signals needed for local policy refinement. We identify this bottleneck as a multi-agent credit assignment problem, which has long been studied in classical multi-agent reinforcement learning (MARL) but remains underaddressed in LLM-based systems. Building on this observation, we propose LangMARL, a framework that brings credit assignment and policy gradient evolution from cooperative MARL into the language space. LangMARL introduces agent-level language credit assignment, pioneers gradient evolution in language space for policy improvement, and summarizes task-relevant causal relations from replayed trajectories to provide dense feedback and improve convergence under sparse rewards. Extensive experiments across diverse cooperative multi-agent tasks demonstrate improved sample efficiency, interpretability, and strong generalization."
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%0 Conference Proceedings
%T LangMARL: Natural Language Multi-Agent Reinforcement Learning via Semantic Credit Assignment
%A Yao, Huaiyuan
%A Da, Longchao
%A Liu, Xiaoou
%A Fleming, Charles
%A Chen, Tianlong
%A Wei, Hua
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yao-etal-2026-langmarl
%X Large language model (LLM) agents struggle to autonomously evolve coordination strategies in dynamic environments, largely because coarse global outcomes obscure the causal signals needed for local policy refinement. We identify this bottleneck as a multi-agent credit assignment problem, which has long been studied in classical multi-agent reinforcement learning (MARL) but remains underaddressed in LLM-based systems. Building on this observation, we propose LangMARL, a framework that brings credit assignment and policy gradient evolution from cooperative MARL into the language space. LangMARL introduces agent-level language credit assignment, pioneers gradient evolution in language space for policy improvement, and summarizes task-relevant causal relations from replayed trajectories to provide dense feedback and improve convergence under sparse rewards. Extensive experiments across diverse cooperative multi-agent tasks demonstrate improved sample efficiency, interpretability, and strong generalization.
%U https://aclanthology.org/2026.emnlp-main.1837/
%P 41793-41807
Markdown (Informal)
[LangMARL: Natural Language Multi-Agent Reinforcement Learning via Semantic Credit Assignment](https://aclanthology.org/2026.emnlp-main.1837/) (Yao et al., EMNLP 2026)
ACL