@inproceedings{wang-etal-2024-reward-modeling,
title = "Reward Modeling Requires Automatic Adjustment Based on Data Quality",
author = "Wang, Binghai and
Zheng, Rui and
Chen, Lu and
Xi, Zhiheng and
Shen, Wei and
Zhou, Yuhao and
Yan, Dong and
Gui, Tao and
Zhang, Qi and
Huang, Xuanjing",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.234/",
doi = "10.18653/v1/2024.findings-emnlp.234",
pages = "4041--4064",
abstract = "In Reinforcement Learning from Human Feedback (RLHF), the reward model plays a crucial role in aligning language model outputs with human values. The human preference data used to train the reward model consists of a prompt and a response pair, with humans annotating which response better aligns with human value preferences. Due to the complexity and subjectivity of the annotation task, multiple organizations including OpenAI and Anthropic report significant noise in the human preference datasets, leading to instability and deviation in reward model training from human values. We discover that the difference in scores assigned to response pairs by the reward model effectively indicates the quality of data, and data of varying qualities show significant distinctions in reward model training. We introduce a method that automatically adjusts reward modeling based on data quality, reducing the impact of noise and making full use of dataset. Experiments on multiple human preference datasets demonstrate that our method stabilizes reward model training and significantly enhances the alignment performance of RLHF."
}
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<abstract>In Reinforcement Learning from Human Feedback (RLHF), the reward model plays a crucial role in aligning language model outputs with human values. The human preference data used to train the reward model consists of a prompt and a response pair, with humans annotating which response better aligns with human value preferences. Due to the complexity and subjectivity of the annotation task, multiple organizations including OpenAI and Anthropic report significant noise in the human preference datasets, leading to instability and deviation in reward model training from human values. We discover that the difference in scores assigned to response pairs by the reward model effectively indicates the quality of data, and data of varying qualities show significant distinctions in reward model training. We introduce a method that automatically adjusts reward modeling based on data quality, reducing the impact of noise and making full use of dataset. Experiments on multiple human preference datasets demonstrate that our method stabilizes reward model training and significantly enhances the alignment performance of RLHF.</abstract>
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%0 Conference Proceedings
%T Reward Modeling Requires Automatic Adjustment Based on Data Quality
%A Wang, Binghai
%A Zheng, Rui
%A Chen, Lu
%A Xi, Zhiheng
%A Shen, Wei
%A Zhou, Yuhao
%A Yan, Dong
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wang-etal-2024-reward-modeling
%X In Reinforcement Learning from Human Feedback (RLHF), the reward model plays a crucial role in aligning language model outputs with human values. The human preference data used to train the reward model consists of a prompt and a response pair, with humans annotating which response better aligns with human value preferences. Due to the complexity and subjectivity of the annotation task, multiple organizations including OpenAI and Anthropic report significant noise in the human preference datasets, leading to instability and deviation in reward model training from human values. We discover that the difference in scores assigned to response pairs by the reward model effectively indicates the quality of data, and data of varying qualities show significant distinctions in reward model training. We introduce a method that automatically adjusts reward modeling based on data quality, reducing the impact of noise and making full use of dataset. Experiments on multiple human preference datasets demonstrate that our method stabilizes reward model training and significantly enhances the alignment performance of RLHF.
%R 10.18653/v1/2024.findings-emnlp.234
%U https://aclanthology.org/2024.findings-emnlp.234/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.234
%P 4041-4064
Markdown (Informal)
[Reward Modeling Requires Automatic Adjustment Based on Data Quality](https://aclanthology.org/2024.findings-emnlp.234/) (Wang et al., Findings 2024)
ACL
- Binghai Wang, Rui Zheng, Lu Chen, Zhiheng Xi, Wei Shen, Yuhao Zhou, Dong Yan, Tao Gui, Qi Zhang, and Xuanjing Huang. 2024. Reward Modeling Requires Automatic Adjustment Based on Data Quality. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4041–4064, Miami, Florida, USA. Association for Computational Linguistics.