@inproceedings{sun-etal-2025-curiosity,
title = "Curiosity-Driven Reinforcement Learning from Human Feedback",
author = "Sun, Haoran and
Chai, Yekun and
Wang, Shuohuan and
Sun, Yu and
Wu, Hua and
Wang, Haifeng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1146/",
doi = "10.18653/v1/2025.acl-long.1146",
pages = "23517--23534",
ISBN = "979-8-89176-251-0",
abstract = "Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences, but often at the cost of reduced output diversity. This trade-off between diversity and alignment quality remains a significant challenge. Drawing inspiration from curiosity-driven exploration in reinforcement learning, we introduce curiosity-driven RLHF (CD-RLHF), a framework that incorporates intrinsic rewards for novel states, alongside traditional sparse extrinsic rewards, to optimize both output diversity and alignment quality. We demonstrate the effectiveness of CD-RLHF through extensive experiments on a range of tasks, including text summarization and instruction following. Our approach achieves significant gains in diversity on multiple diversity-oriented metrics while maintaining alignment with human preferences comparable to standard RLHF. We will make our code publicly available."
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<abstract>Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences, but often at the cost of reduced output diversity. This trade-off between diversity and alignment quality remains a significant challenge. Drawing inspiration from curiosity-driven exploration in reinforcement learning, we introduce curiosity-driven RLHF (CD-RLHF), a framework that incorporates intrinsic rewards for novel states, alongside traditional sparse extrinsic rewards, to optimize both output diversity and alignment quality. We demonstrate the effectiveness of CD-RLHF through extensive experiments on a range of tasks, including text summarization and instruction following. Our approach achieves significant gains in diversity on multiple diversity-oriented metrics while maintaining alignment with human preferences comparable to standard RLHF. We will make our code publicly available.</abstract>
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%0 Conference Proceedings
%T Curiosity-Driven Reinforcement Learning from Human Feedback
%A Sun, Haoran
%A Chai, Yekun
%A Wang, Shuohuan
%A Sun, Yu
%A Wu, Hua
%A Wang, Haifeng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F sun-etal-2025-curiosity
%X Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences, but often at the cost of reduced output diversity. This trade-off between diversity and alignment quality remains a significant challenge. Drawing inspiration from curiosity-driven exploration in reinforcement learning, we introduce curiosity-driven RLHF (CD-RLHF), a framework that incorporates intrinsic rewards for novel states, alongside traditional sparse extrinsic rewards, to optimize both output diversity and alignment quality. We demonstrate the effectiveness of CD-RLHF through extensive experiments on a range of tasks, including text summarization and instruction following. Our approach achieves significant gains in diversity on multiple diversity-oriented metrics while maintaining alignment with human preferences comparable to standard RLHF. We will make our code publicly available.
%R 10.18653/v1/2025.acl-long.1146
%U https://aclanthology.org/2025.acl-long.1146/
%U https://doi.org/10.18653/v1/2025.acl-long.1146
%P 23517-23534
Markdown (Informal)
[Curiosity-Driven Reinforcement Learning from Human Feedback](https://aclanthology.org/2025.acl-long.1146/) (Sun et al., ACL 2025)
ACL
- Haoran Sun, Yekun Chai, Shuohuan Wang, Yu Sun, Hua Wu, and Haifeng Wang. 2025. Curiosity-Driven Reinforcement Learning from Human Feedback. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23517–23534, Vienna, Austria. Association for Computational Linguistics.