@inproceedings{liao-etal-2025-enhancing,
title = "Enhancing Efficiency and Exploration in Reinforcement Learning for {LLM}s",
author = "Liao, Mengqi and
Xi, Xiangyu and
Ruinian, Chen and
Leng, Jia and
Hu, Yangen and
Zeng, Ke and
Liu, Shuai and
Wan, Huaiyu",
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.75/",
doi = "10.18653/v1/2025.emnlp-main.75",
pages = "1451--1463",
ISBN = "979-8-89176-332-6",
abstract = "Reasoning large language models (LLMs) excel in complex tasks, which has drawn significant attention to reinforcement learning (RL) for LLMs. However, existing approaches allocate an equal number of rollouts to all questions during the RL process, which is inefficient. This inefficiency stems from the fact that training on simple questions yields limited gains, whereas more rollouts are needed for challenging questions to sample correct answers. Furthermore, while RL improves response precision, it limits the model{'}s exploration ability, potentially resulting in a performance cap below that of the base model prior to RL. To address these issues, we propose a mechanism for dynamically allocating rollout budgets based on the difficulty of the problems, enabling more efficient RL training. Additionally, we introduce an adaptive dynamic temperature adjustment strategy to maintain the entropy at a stable level, thereby encouraging sufficient exploration. This enables LLMs to improve response precision while preserving their exploratory ability to uncover potential correct pathways. The code and data is available on: https://anonymous.4open.science/r/E3-RL4LLMs-DB28"
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<abstract>Reasoning large language models (LLMs) excel in complex tasks, which has drawn significant attention to reinforcement learning (RL) for LLMs. However, existing approaches allocate an equal number of rollouts to all questions during the RL process, which is inefficient. This inefficiency stems from the fact that training on simple questions yields limited gains, whereas more rollouts are needed for challenging questions to sample correct answers. Furthermore, while RL improves response precision, it limits the model’s exploration ability, potentially resulting in a performance cap below that of the base model prior to RL. To address these issues, we propose a mechanism for dynamically allocating rollout budgets based on the difficulty of the problems, enabling more efficient RL training. Additionally, we introduce an adaptive dynamic temperature adjustment strategy to maintain the entropy at a stable level, thereby encouraging sufficient exploration. This enables LLMs to improve response precision while preserving their exploratory ability to uncover potential correct pathways. The code and data is available on: https://anonymous.4open.science/r/E3-RL4LLMs-DB28</abstract>
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%0 Conference Proceedings
%T Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs
%A Liao, Mengqi
%A Xi, Xiangyu
%A Ruinian, Chen
%A Leng, Jia
%A Hu, Yangen
%A Zeng, Ke
%A Liu, Shuai
%A Wan, Huaiyu
%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 liao-etal-2025-enhancing
%X Reasoning large language models (LLMs) excel in complex tasks, which has drawn significant attention to reinforcement learning (RL) for LLMs. However, existing approaches allocate an equal number of rollouts to all questions during the RL process, which is inefficient. This inefficiency stems from the fact that training on simple questions yields limited gains, whereas more rollouts are needed for challenging questions to sample correct answers. Furthermore, while RL improves response precision, it limits the model’s exploration ability, potentially resulting in a performance cap below that of the base model prior to RL. To address these issues, we propose a mechanism for dynamically allocating rollout budgets based on the difficulty of the problems, enabling more efficient RL training. Additionally, we introduce an adaptive dynamic temperature adjustment strategy to maintain the entropy at a stable level, thereby encouraging sufficient exploration. This enables LLMs to improve response precision while preserving their exploratory ability to uncover potential correct pathways. The code and data is available on: https://anonymous.4open.science/r/E3-RL4LLMs-DB28
%R 10.18653/v1/2025.emnlp-main.75
%U https://aclanthology.org/2025.emnlp-main.75/
%U https://doi.org/10.18653/v1/2025.emnlp-main.75
%P 1451-1463
Markdown (Informal)
[Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs](https://aclanthology.org/2025.emnlp-main.75/) (Liao et al., EMNLP 2025)
ACL
- Mengqi Liao, Xiangyu Xi, Chen Ruinian, Jia Leng, Yangen Hu, Ke Zeng, Shuai Liu, and Huaiyu Wan. 2025. Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 1451–1463, Suzhou, China. Association for Computational Linguistics.