@inproceedings{jin-etal-2026-tagpr,
title = "{T}ag{PR}: Tag-Guided Process Supervision for Personalization Reasoning in Large Language Models",
author = "Jin, Song and
Zhang, Juntian and
Lyu, Ruyu and
Liu, Yong and
Zhang, Xun and
Zhang, Yufei and
Jiang, Fei and
Yin, Guojun and
Lin, Wei and
Yan, Rui",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1675/",
pages = "38104--38127",
ISBN = "979-8-89176-459-0",
abstract = "Recent advancements have endowed Large Language Models with impressive general reasoning capabilities. However, these reasoning models often perform worse than non-reasoning models on personalization tasks. While some methods use outcome-based RL to improve personalization reasoning, they fail to supervise the reasoning process. As a result, models may reach correct answers through flawed reasoning chains, limiting further improvement. To address this, we propose TagPR, a novel framework that adds semantic tags to the reasoning process for step-by-step guidance. TagPR first automatically generates a structured, tagged dataset for Supervised Fine-Tuning. It then employs a multi-stage RL process guided by a composite reward signal, which integrates tag-based process supervision with a novel Personalization Reward Model with User Embeddings to achieve fine-grained alignment with user-specific logic. Extensive experiments on public LaMP, LongLaMP, PGraphRAG, and a self-constructed dataset demonstrate that our approach achieves state-of-the-art results, delivering an average improvement of 32.65{\%} over the base model across all LaMP benchmark tasks. Our work demonstrates that tag-guided process supervision is an effective approach for personalization reasoning."
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<abstract>Recent advancements have endowed Large Language Models with impressive general reasoning capabilities. However, these reasoning models often perform worse than non-reasoning models on personalization tasks. While some methods use outcome-based RL to improve personalization reasoning, they fail to supervise the reasoning process. As a result, models may reach correct answers through flawed reasoning chains, limiting further improvement. To address this, we propose TagPR, a novel framework that adds semantic tags to the reasoning process for step-by-step guidance. TagPR first automatically generates a structured, tagged dataset for Supervised Fine-Tuning. It then employs a multi-stage RL process guided by a composite reward signal, which integrates tag-based process supervision with a novel Personalization Reward Model with User Embeddings to achieve fine-grained alignment with user-specific logic. Extensive experiments on public LaMP, LongLaMP, PGraphRAG, and a self-constructed dataset demonstrate that our approach achieves state-of-the-art results, delivering an average improvement of 32.65% over the base model across all LaMP benchmark tasks. Our work demonstrates that tag-guided process supervision is an effective approach for personalization reasoning.</abstract>
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%0 Conference Proceedings
%T TagPR: Tag-Guided Process Supervision for Personalization Reasoning in Large Language Models
%A Jin, Song
%A Zhang, Juntian
%A Lyu, Ruyu
%A Liu, Yong
%A Zhang, Xun
%A Zhang, Yufei
%A Jiang, Fei
%A Yin, Guojun
%A Lin, Wei
%A Yan, Rui
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jin-etal-2026-tagpr
%X Recent advancements have endowed Large Language Models with impressive general reasoning capabilities. However, these reasoning models often perform worse than non-reasoning models on personalization tasks. While some methods use outcome-based RL to improve personalization reasoning, they fail to supervise the reasoning process. As a result, models may reach correct answers through flawed reasoning chains, limiting further improvement. To address this, we propose TagPR, a novel framework that adds semantic tags to the reasoning process for step-by-step guidance. TagPR first automatically generates a structured, tagged dataset for Supervised Fine-Tuning. It then employs a multi-stage RL process guided by a composite reward signal, which integrates tag-based process supervision with a novel Personalization Reward Model with User Embeddings to achieve fine-grained alignment with user-specific logic. Extensive experiments on public LaMP, LongLaMP, PGraphRAG, and a self-constructed dataset demonstrate that our approach achieves state-of-the-art results, delivering an average improvement of 32.65% over the base model across all LaMP benchmark tasks. Our work demonstrates that tag-guided process supervision is an effective approach for personalization reasoning.
%U https://aclanthology.org/2026.emnlp-main.1675/
%P 38104-38127
Markdown (Informal)
[TagPR: Tag-Guided Process Supervision for Personalization Reasoning in Large Language Models](https://aclanthology.org/2026.emnlp-main.1675/) (Jin et al., EMNLP 2026)
ACL
- Song Jin, Juntian Zhang, Ruyu Lyu, Yong Liu, Xun Zhang, Yufei Zhang, Fei Jiang, Guojun Yin, Wei Lin, and Rui Yan. 2026. TagPR: Tag-Guided Process Supervision for Personalization Reasoning in Large Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 38104–38127, Budapest, Hungary. Association for Computational Linguistics.