@inproceedings{fu-etal-2025-rlae,
title = "{RLAE}: Reinforcement Learning-Assisted Ensemble for {LLM}s",
author = "Fu, Yuqian and
Zhu, Yuanheng and
Chai, Jiajun and
Yin, Guojun and
Lin, Wei and
Zhang, Qichao and
Zhao, Dongbin",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.680/",
pages = "13463--13477",
ISBN = "979-8-89176-332-6",
abstract = "Ensembling large language models (LLMs) can effectively combine diverse strengths of different models, offering a promising approach to enhance performance across various tasks. However, existing methods typically rely on fixed weighting strategies that fail to adapt to the dynamic, context-dependent characteristics of LLM capabilities. In this work, we propose **R**einforcement **L**earning-**A**ssisted **E**nsemble for LLMs (RLAE), a novel framework that reformulates LLM ensemble through the lens of a Markov Decision Process (MDP). Our approach introduces a RL agent that dynamically adjusts ensemble weights by considering both input context and intermediate generation states, with the agent being trained using rewards that directly correspond to the quality of final outputs. We implement RLAE using both single-agent and multi-agent reinforcement learning algorithms ($\text{RLAE}\_\text{PPO}$ and $\text{RLAE}\_\text{MAPPO}$ ), demonstrating substantial improvements over conventional ensemble methods. Extensive evaluations on a diverse set of tasks show that RLAE outperforms existing approaches by up to $3.3\\%$ accuracy points, offering a more effective framework for LLM ensembling. Furthermore, our method exhibits superior generalization capabilities across different tasks without the need for retraining, while simultaneously achieving lower time latency. The source code is available at here."
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<abstract>Ensembling large language models (LLMs) can effectively combine diverse strengths of different models, offering a promising approach to enhance performance across various tasks. However, existing methods typically rely on fixed weighting strategies that fail to adapt to the dynamic, context-dependent characteristics of LLM capabilities. In this work, we propose **R**einforcement **L**earning-**A**ssisted **E**nsemble for LLMs (RLAE), a novel framework that reformulates LLM ensemble through the lens of a Markov Decision Process (MDP). Our approach introduces a RL agent that dynamically adjusts ensemble weights by considering both input context and intermediate generation states, with the agent being trained using rewards that directly correspond to the quality of final outputs. We implement RLAE using both single-agent and multi-agent reinforcement learning algorithms (\textRLAE_\textPPO and \textRLAE_\textMAPPO ), demonstrating substantial improvements over conventional ensemble methods. Extensive evaluations on a diverse set of tasks show that RLAE outperforms existing approaches by up to 3.3\% accuracy points, offering a more effective framework for LLM ensembling. Furthermore, our method exhibits superior generalization capabilities across different tasks without the need for retraining, while simultaneously achieving lower time latency. The source code is available at here.</abstract>
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%0 Conference Proceedings
%T RLAE: Reinforcement Learning-Assisted Ensemble for LLMs
%A Fu, Yuqian
%A Zhu, Yuanheng
%A Chai, Jiajun
%A Yin, Guojun
%A Lin, Wei
%A Zhang, Qichao
%A Zhao, Dongbin
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F fu-etal-2025-rlae
%X Ensembling large language models (LLMs) can effectively combine diverse strengths of different models, offering a promising approach to enhance performance across various tasks. However, existing methods typically rely on fixed weighting strategies that fail to adapt to the dynamic, context-dependent characteristics of LLM capabilities. In this work, we propose **R**einforcement **L**earning-**A**ssisted **E**nsemble for LLMs (RLAE), a novel framework that reformulates LLM ensemble through the lens of a Markov Decision Process (MDP). Our approach introduces a RL agent that dynamically adjusts ensemble weights by considering both input context and intermediate generation states, with the agent being trained using rewards that directly correspond to the quality of final outputs. We implement RLAE using both single-agent and multi-agent reinforcement learning algorithms (\textRLAE_\textPPO and \textRLAE_\textMAPPO ), demonstrating substantial improvements over conventional ensemble methods. Extensive evaluations on a diverse set of tasks show that RLAE outperforms existing approaches by up to 3.3\% accuracy points, offering a more effective framework for LLM ensembling. Furthermore, our method exhibits superior generalization capabilities across different tasks without the need for retraining, while simultaneously achieving lower time latency. The source code is available at here.
%U https://aclanthology.org/2025.emnlp-main.680/
%P 13463-13477
Markdown (Informal)
[RLAE: Reinforcement Learning-Assisted Ensemble for LLMs](https://aclanthology.org/2025.emnlp-main.680/) (Fu et al., EMNLP 2025)
ACL
- Yuqian Fu, Yuanheng Zhu, Jiajun Chai, Guojun Yin, Wei Lin, Qichao Zhang, and Dongbin Zhao. 2025. RLAE: Reinforcement Learning-Assisted Ensemble for LLMs. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 13463–13477, Suzhou, China. Association for Computational Linguistics.