@inproceedings{wang-etal-2025-adapting,
title = "Adapting {LLM} Agents with Universal Communication Feedback",
author = "Wang, Kuan and
Lu, Yadong and
Santacroce, Michael and
Gong, Yeyun and
Zhang, Chao and
Shen, Yelong",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.339/",
doi = "10.18653/v1/2025.findings-naacl.339",
pages = "6090--6107",
ISBN = "979-8-89176-195-7",
abstract = "Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. To facilitate the training for these agents with both linguistic feedback and non-linguistic reward signals, we introduce Learning through Communication (LTC). We design a universal buffer to store all the feedback, and an iterative pipeline to enable an LLM agent to explore and update its policy in an given environment. To optimize agent interactions for task-specific learning with our universal buffer and pipeline, we introduce diverse communication patterns tailored for both single-agent and multi-agent environments. We evaluate the efficacy of our LTC approach on four diverse datasets: ALFWorld (single-agent), HotpotQA (multi-agent collaboration), Chameleon (multi-agent competition), and GSM8k (multi-agent teacher-student). On these data sets, LTC outperforms the supervised instruction fine-tuning baselines by 3.6{\%} to 12{\%}. These results highlight the versatility and efficiency of LTC in facilitating online adaptation for LLM agents."
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<abstract>Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. To facilitate the training for these agents with both linguistic feedback and non-linguistic reward signals, we introduce Learning through Communication (LTC). We design a universal buffer to store all the feedback, and an iterative pipeline to enable an LLM agent to explore and update its policy in an given environment. To optimize agent interactions for task-specific learning with our universal buffer and pipeline, we introduce diverse communication patterns tailored for both single-agent and multi-agent environments. We evaluate the efficacy of our LTC approach on four diverse datasets: ALFWorld (single-agent), HotpotQA (multi-agent collaboration), Chameleon (multi-agent competition), and GSM8k (multi-agent teacher-student). On these data sets, LTC outperforms the supervised instruction fine-tuning baselines by 3.6% to 12%. These results highlight the versatility and efficiency of LTC in facilitating online adaptation for LLM agents.</abstract>
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%0 Conference Proceedings
%T Adapting LLM Agents with Universal Communication Feedback
%A Wang, Kuan
%A Lu, Yadong
%A Santacroce, Michael
%A Gong, Yeyun
%A Zhang, Chao
%A Shen, Yelong
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F wang-etal-2025-adapting
%X Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. To facilitate the training for these agents with both linguistic feedback and non-linguistic reward signals, we introduce Learning through Communication (LTC). We design a universal buffer to store all the feedback, and an iterative pipeline to enable an LLM agent to explore and update its policy in an given environment. To optimize agent interactions for task-specific learning with our universal buffer and pipeline, we introduce diverse communication patterns tailored for both single-agent and multi-agent environments. We evaluate the efficacy of our LTC approach on four diverse datasets: ALFWorld (single-agent), HotpotQA (multi-agent collaboration), Chameleon (multi-agent competition), and GSM8k (multi-agent teacher-student). On these data sets, LTC outperforms the supervised instruction fine-tuning baselines by 3.6% to 12%. These results highlight the versatility and efficiency of LTC in facilitating online adaptation for LLM agents.
%R 10.18653/v1/2025.findings-naacl.339
%U https://aclanthology.org/2025.findings-naacl.339/
%U https://doi.org/10.18653/v1/2025.findings-naacl.339
%P 6090-6107
Markdown (Informal)
[Adapting LLM Agents with Universal Communication Feedback](https://aclanthology.org/2025.findings-naacl.339/) (Wang et al., Findings 2025)
ACL
- Kuan Wang, Yadong Lu, Michael Santacroce, Yeyun Gong, Chao Zhang, and Yelong Shen. 2025. Adapting LLM Agents with Universal Communication Feedback. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 6090–6107, Albuquerque, New Mexico. Association for Computational Linguistics.