@inproceedings{pan-etal-2026-rest,
title = "{REST}: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once",
author = "Pan, Zhuoshi and
Pei, Qizhi and
Li, Yu and
Tang, Zinan and
Sun, QiYao and
Zhao, H. Vicky and
He, Conghui and
Wu, Lijun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1296/",
pages = "28110--28140",
ISBN = "979-8-89176-390-6",
abstract = "Recent Large Reasoning Models (LRMs) have achieved remarkable progress, yet their evaluation still relies on a narrow paradigm: evaluating one question at a time. This single-question setup suffers from two major limitations: (1) vulnerability to data contamination and diminishing difficulty, forcing costly creation of new questions with significant human effort, (2) failure to evaluate models under multi-context pressure, a key requirement for real-world deployment. To bridge this gap, we present **REST** (Reasoning Evaluation through Simultaneous Testing), a stress-testing framework that exposes LRMs to multiple problems simultaneously. Beyond basic reasoning, REST evaluates two under-tested capabilities: *contextual priority allocation* and *robustness against contextual interference*. Our evaluation of more than **30** advanced reasoning models on **9** reasoning benchmarks reveals several striking findings: Even state-of-the-art (SOTA) models such as ***DeepSeek-R1 exhibit substantial performance degradation under stress testing***, challenging the prevailing assumption that ``LLMs are multi-problem solvers''. Crucially, ***REST demonstrates stronger discriminative power*** than existing benchmarks, revealing performance gaps among models that exhibit similar, near-ceiling performance under traditional evaluation. Some key insights emerge from our analysis: (1) the ***{''}overthinking trap''*** is a critical factor contributing to the performance degradation; (2) models trained with the ***{''}Long2Short'' technique preserve more of their single-problem accuracy*** under REST, outperforming their standard-trained counterparts. These results establish REST as a cost-efficient, future-proof evaluation paradigm while reducing reliance on continuous human annotation. Code is available at https://github.com/opendatalab/REST."
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<abstract>Recent Large Reasoning Models (LRMs) have achieved remarkable progress, yet their evaluation still relies on a narrow paradigm: evaluating one question at a time. This single-question setup suffers from two major limitations: (1) vulnerability to data contamination and diminishing difficulty, forcing costly creation of new questions with significant human effort, (2) failure to evaluate models under multi-context pressure, a key requirement for real-world deployment. To bridge this gap, we present **REST** (Reasoning Evaluation through Simultaneous Testing), a stress-testing framework that exposes LRMs to multiple problems simultaneously. Beyond basic reasoning, REST evaluates two under-tested capabilities: *contextual priority allocation* and *robustness against contextual interference*. Our evaluation of more than **30** advanced reasoning models on **9** reasoning benchmarks reveals several striking findings: Even state-of-the-art (SOTA) models such as ***DeepSeek-R1 exhibit substantial performance degradation under stress testing***, challenging the prevailing assumption that “LLMs are multi-problem solvers”. Crucially, ***REST demonstrates stronger discriminative power*** than existing benchmarks, revealing performance gaps among models that exhibit similar, near-ceiling performance under traditional evaluation. Some key insights emerge from our analysis: (1) the ***”overthinking trap”*** is a critical factor contributing to the performance degradation; (2) models trained with the ***”Long2Short” technique preserve more of their single-problem accuracy*** under REST, outperforming their standard-trained counterparts. These results establish REST as a cost-efficient, future-proof evaluation paradigm while reducing reliance on continuous human annotation. Code is available at https://github.com/opendatalab/REST.</abstract>
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%0 Conference Proceedings
%T REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once
%A Pan, Zhuoshi
%A Pei, Qizhi
%A Li, Yu
%A Tang, Zinan
%A Sun, QiYao
%A Zhao, H. Vicky
%A He, Conghui
%A Wu, Lijun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F pan-etal-2026-rest
%X Recent Large Reasoning Models (LRMs) have achieved remarkable progress, yet their evaluation still relies on a narrow paradigm: evaluating one question at a time. This single-question setup suffers from two major limitations: (1) vulnerability to data contamination and diminishing difficulty, forcing costly creation of new questions with significant human effort, (2) failure to evaluate models under multi-context pressure, a key requirement for real-world deployment. To bridge this gap, we present **REST** (Reasoning Evaluation through Simultaneous Testing), a stress-testing framework that exposes LRMs to multiple problems simultaneously. Beyond basic reasoning, REST evaluates two under-tested capabilities: *contextual priority allocation* and *robustness against contextual interference*. Our evaluation of more than **30** advanced reasoning models on **9** reasoning benchmarks reveals several striking findings: Even state-of-the-art (SOTA) models such as ***DeepSeek-R1 exhibit substantial performance degradation under stress testing***, challenging the prevailing assumption that “LLMs are multi-problem solvers”. Crucially, ***REST demonstrates stronger discriminative power*** than existing benchmarks, revealing performance gaps among models that exhibit similar, near-ceiling performance under traditional evaluation. Some key insights emerge from our analysis: (1) the ***”overthinking trap”*** is a critical factor contributing to the performance degradation; (2) models trained with the ***”Long2Short” technique preserve more of their single-problem accuracy*** under REST, outperforming their standard-trained counterparts. These results establish REST as a cost-efficient, future-proof evaluation paradigm while reducing reliance on continuous human annotation. Code is available at https://github.com/opendatalab/REST.
%U https://aclanthology.org/2026.acl-long.1296/
%P 28110-28140
Markdown (Informal)
[REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once](https://aclanthology.org/2026.acl-long.1296/) (Pan et al., ACL 2026)
ACL
- Zhuoshi Pan, Qizhi Pei, Yu Li, Zinan Tang, QiYao Sun, H. Vicky Zhao, Conghui He, and Lijun Wu. 2026. REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 28110–28140, San Diego, California, United States. Association for Computational Linguistics.