@inproceedings{lai-etal-2026-adar,
title = "{A}da{R}: A Framework for Equipping {LLM}s with Adaptive Reasoning",
author = "Lai, Zhejian and
Geng, Xiang and
Wang, Zhijun and
Bai, Yang and
Li, Jiahuan and
Weng, Rongxiang and
Wang, Jingang and
Cao, Xuezhi and
Cai, Xunliang and
Huang, Shujian",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.960/",
pages = "20288--20306",
ISBN = "979-8-89176-462-0",
abstract = "Mathematical reasoning is a primary indicator of large language models' (LLMs) intelligence. However, existing LLMs exhibit failures in robustness and generalization. This paper attributes these deficiencies to spurious reasoning, wherein generated reasoning traces are driven by superficial correlations, leading models to blindly reproduce memorized patterns from the training data. To address this challenge, we propose the AdaR framework to equip LLMs with adaptive reasoning, wherein models establish correct correlations between query templates and reasoning processes. AdaR automatically synthesizes logically equivalent queries by varying variable values and trains models using Reinforcement Learning with Verifiable Rewards (RLVR) to penalize spurious logic while encouraging adaptive logic. To ensure data quality, we extract the problem-solving logic from the original query, generate the corresponding answer through code execution, and then apply a sanity check. Experimental results demonstrate that AdaR achieves substantial improvements in mathematical reasoning while maintaining high data efficiency. Furthermore, even advanced LLMs still exhibit deficiencies in robustness and generalization, which our work effectively mitigates."
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%0 Conference Proceedings
%T AdaR: A Framework for Equipping LLMs with Adaptive Reasoning
%A Lai, Zhejian
%A Geng, Xiang
%A Wang, Zhijun
%A Bai, Yang
%A Li, Jiahuan
%A Weng, Rongxiang
%A Wang, Jingang
%A Cao, Xuezhi
%A Cai, Xunliang
%A Huang, Shujian
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lai-etal-2026-adar
%X Mathematical reasoning is a primary indicator of large language models’ (LLMs) intelligence. However, existing LLMs exhibit failures in robustness and generalization. This paper attributes these deficiencies to spurious reasoning, wherein generated reasoning traces are driven by superficial correlations, leading models to blindly reproduce memorized patterns from the training data. To address this challenge, we propose the AdaR framework to equip LLMs with adaptive reasoning, wherein models establish correct correlations between query templates and reasoning processes. AdaR automatically synthesizes logically equivalent queries by varying variable values and trains models using Reinforcement Learning with Verifiable Rewards (RLVR) to penalize spurious logic while encouraging adaptive logic. To ensure data quality, we extract the problem-solving logic from the original query, generate the corresponding answer through code execution, and then apply a sanity check. Experimental results demonstrate that AdaR achieves substantial improvements in mathematical reasoning while maintaining high data efficiency. Furthermore, even advanced LLMs still exhibit deficiencies in robustness and generalization, which our work effectively mitigates.
%U https://aclanthology.org/2026.findings-emnlp.960/
%P 20288-20306
Markdown (Informal)
[AdaR: A Framework for Equipping LLMs with Adaptive Reasoning](https://aclanthology.org/2026.findings-emnlp.960/) (Lai et al., Findings 2026)
ACL
- Zhejian Lai, Xiang Geng, Zhijun Wang, Yang Bai, Jiahuan Li, Rongxiang Weng, Jingang Wang, Xuezhi Cao, Xunliang Cai, and Shujian Huang. 2026. AdaR: A Framework for Equipping LLMs with Adaptive Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20288–20306, Budapest, Hungary. Association for Computational Linguistics.