@inproceedings{burgess-etal-2026-papersearchqa,
title = "{P}aper{S}earch{QA}: Learning to Search and Reason over Scientific Papers with {RLVR}",
author = "Burgess, James and
Hansen, Jan N. and
Peng, Duo and
Zhang, Yuhui and
Lozano, Alejandro and
Sun, Min Woo and
Lundberg, Emma and
Yeung-Levy, Serena",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.88/",
pages = "1979--1997",
ISBN = "979-8-89176-380-7",
abstract = "Search agents are language models (LMs) that reason and search knowledge bases (or the web) to answer questions; recent methods supervise only the final answer accuracy using reinforcement learning with verifiable rewards (RLVR). Most RLVR search agents tackle general-domain QA, which limits their relevance to technical AI systems in science, engineering, and medicine. In this work we propose training agents to search and reason over scientific papers {--} this tests technical question-answering, it is directly relevant to real scientists, and the capabilities will be crucial to future AI Scientist systems. Concretely, we release a search corpus of 16 million biomedical paper abstracts and construct a challenging factoid QA dataset called PaperSearchQA with 60k samples answerable from the corpus, along with benchmarks. We train search agents in this environment to outperform non-RL retrieval baselines; we also perform further quantitative analysis and observe interesting agent behaviors like planning, reasoning, and self-verification. Our corpus, datasets, and benchmarks are usable with the popular Search-R1 codebase for RLVR training; they are available on Hugging Face. Finally, our data creation methods are scalable and easily extendable to other scientific domains."
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<abstract>Search agents are language models (LMs) that reason and search knowledge bases (or the web) to answer questions; recent methods supervise only the final answer accuracy using reinforcement learning with verifiable rewards (RLVR). Most RLVR search agents tackle general-domain QA, which limits their relevance to technical AI systems in science, engineering, and medicine. In this work we propose training agents to search and reason over scientific papers – this tests technical question-answering, it is directly relevant to real scientists, and the capabilities will be crucial to future AI Scientist systems. Concretely, we release a search corpus of 16 million biomedical paper abstracts and construct a challenging factoid QA dataset called PaperSearchQA with 60k samples answerable from the corpus, along with benchmarks. We train search agents in this environment to outperform non-RL retrieval baselines; we also perform further quantitative analysis and observe interesting agent behaviors like planning, reasoning, and self-verification. Our corpus, datasets, and benchmarks are usable with the popular Search-R1 codebase for RLVR training; they are available on Hugging Face. Finally, our data creation methods are scalable and easily extendable to other scientific domains.</abstract>
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%0 Conference Proceedings
%T PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR
%A Burgess, James
%A Hansen, Jan N.
%A Peng, Duo
%A Zhang, Yuhui
%A Lozano, Alejandro
%A Sun, Min Woo
%A Lundberg, Emma
%A Yeung-Levy, Serena
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F burgess-etal-2026-papersearchqa
%X Search agents are language models (LMs) that reason and search knowledge bases (or the web) to answer questions; recent methods supervise only the final answer accuracy using reinforcement learning with verifiable rewards (RLVR). Most RLVR search agents tackle general-domain QA, which limits their relevance to technical AI systems in science, engineering, and medicine. In this work we propose training agents to search and reason over scientific papers – this tests technical question-answering, it is directly relevant to real scientists, and the capabilities will be crucial to future AI Scientist systems. Concretely, we release a search corpus of 16 million biomedical paper abstracts and construct a challenging factoid QA dataset called PaperSearchQA with 60k samples answerable from the corpus, along with benchmarks. We train search agents in this environment to outperform non-RL retrieval baselines; we also perform further quantitative analysis and observe interesting agent behaviors like planning, reasoning, and self-verification. Our corpus, datasets, and benchmarks are usable with the popular Search-R1 codebase for RLVR training; they are available on Hugging Face. Finally, our data creation methods are scalable and easily extendable to other scientific domains.
%U https://aclanthology.org/2026.eacl-long.88/
%P 1979-1997
Markdown (Informal)
[PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR](https://aclanthology.org/2026.eacl-long.88/) (Burgess et al., EACL 2026)
ACL
- James Burgess, Jan N. Hansen, Duo Peng, Yuhui Zhang, Alejandro Lozano, Min Woo Sun, Emma Lundberg, and Serena Yeung-Levy. 2026. PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1979–1997, Rabat, Morocco. Association for Computational Linguistics.