@inproceedings{lv-etal-2026-learning-query,
title = "Learning Query-Specific Rubrics from Human Preferences for {D}eep{R}esearch Report Generation",
author = "Lv, Changze and
Zhou, Jie and
Zhao, Wentao and
Xu, Jingwen and
Dou, Shihan and
Huang, Zisu and
Tian, Muzhao and
Wang, Xiaohua and
Liu, Yang and
Zhou, Pluto and
Gui, Tao and
Tian, Le and
Xiao, Zhou and
Zheng, Xiaoqing and
Huang, Xuanjing and
Zhou, Jie",
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.1841/",
pages = "41870--41897",
ISBN = "979-8-89176-459-0",
abstract = "Nowadays, developing reliable DeepResearch-style long-form report generation remains challenging, as training and evaluation lack verifiable reward signals. Accordingly, rubric-based evaluation has become a common practice. However, existing approaches either rely on coarse, pre-defined rubrics that lack sufficient granularity or depend on manually constructed query-specific rubrics that are costly and difficult to scale. In this paper, we propose a pipeline to train preference-grounded query-specific rubric generators tailored for DeepResearch report generation. We first construct a dataset of DeepResearch-style queries annotated with human preferences over paired reports, and train rubric generators via reinforcement learning with a hybrid reward combining preference consistency, format validity, and LLM-based rubric evaluation. We evaluate the resulting rubric generators in two stages. First, on a held-out human-preference test set, the learned rubrics discriminate preferred from rejected reports more effectively than generic, prompted, or SFT-trained rubric alternatives. Second, when used as reward signals to train DeepResearch systems, our rubric generators yield substantial performance gains under both a simple single-agent ReAct framework and a complex multi-agent workflow on the DeepResearch Bench. Our code and model are available at https://huggingface.co/fdu-lcz/rubric{\_}generator."
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<abstract>Nowadays, developing reliable DeepResearch-style long-form report generation remains challenging, as training and evaluation lack verifiable reward signals. Accordingly, rubric-based evaluation has become a common practice. However, existing approaches either rely on coarse, pre-defined rubrics that lack sufficient granularity or depend on manually constructed query-specific rubrics that are costly and difficult to scale. In this paper, we propose a pipeline to train preference-grounded query-specific rubric generators tailored for DeepResearch report generation. We first construct a dataset of DeepResearch-style queries annotated with human preferences over paired reports, and train rubric generators via reinforcement learning with a hybrid reward combining preference consistency, format validity, and LLM-based rubric evaluation. We evaluate the resulting rubric generators in two stages. First, on a held-out human-preference test set, the learned rubrics discriminate preferred from rejected reports more effectively than generic, prompted, or SFT-trained rubric alternatives. Second, when used as reward signals to train DeepResearch systems, our rubric generators yield substantial performance gains under both a simple single-agent ReAct framework and a complex multi-agent workflow on the DeepResearch Bench. Our code and model are available at https://huggingface.co/fdu-lcz/rubric_generator.</abstract>
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%0 Conference Proceedings
%T Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation
%A Lv, Changze
%A Zhou, Jie
%A Zhao, Wentao
%A Xu, Jingwen
%A Dou, Shihan
%A Huang, Zisu
%A Tian, Muzhao
%A Wang, Xiaohua
%A Liu, Yang
%A Zhou, Pluto
%A Gui, Tao
%A Tian, Le
%A Xiao, Zhou
%A Zheng, Xiaoqing
%A Huang, Xuanjing
%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 lv-etal-2026-learning-query
%X Nowadays, developing reliable DeepResearch-style long-form report generation remains challenging, as training and evaluation lack verifiable reward signals. Accordingly, rubric-based evaluation has become a common practice. However, existing approaches either rely on coarse, pre-defined rubrics that lack sufficient granularity or depend on manually constructed query-specific rubrics that are costly and difficult to scale. In this paper, we propose a pipeline to train preference-grounded query-specific rubric generators tailored for DeepResearch report generation. We first construct a dataset of DeepResearch-style queries annotated with human preferences over paired reports, and train rubric generators via reinforcement learning with a hybrid reward combining preference consistency, format validity, and LLM-based rubric evaluation. We evaluate the resulting rubric generators in two stages. First, on a held-out human-preference test set, the learned rubrics discriminate preferred from rejected reports more effectively than generic, prompted, or SFT-trained rubric alternatives. Second, when used as reward signals to train DeepResearch systems, our rubric generators yield substantial performance gains under both a simple single-agent ReAct framework and a complex multi-agent workflow on the DeepResearch Bench. Our code and model are available at https://huggingface.co/fdu-lcz/rubric_generator.
%U https://aclanthology.org/2026.emnlp-main.1841/
%P 41870-41897
Markdown (Informal)
[Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation](https://aclanthology.org/2026.emnlp-main.1841/) (Lv et al., EMNLP 2026)
ACL
- Changze Lv, Jie Zhou, Wentao Zhao, Jingwen Xu, Shihan Dou, Zisu Huang, Muzhao Tian, Xiaohua Wang, Yang Liu, Pluto Zhou, Tao Gui, Le Tian, Zhou Xiao, Xiaoqing Zheng, Xuanjing Huang, and Jie Zhou. 2026. Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 41870–41897, Budapest, Hungary. Association for Computational Linguistics.