@inproceedings{cui-etal-2026-reward,
title = "A Reward-Guided Dual-Phase Framework for Adaptive Inference-Time Reasoning",
author = "Cui, Yingqian and
Dai, Zhenwei and
He, Pengfei and
He, Bing and
Liu, Hui and
Shi, Zhan and
Tang, Xianfeng and
Zeng, Jingying and
Wang, Suhang and
Xing, Yue and
Tang, Jiliang and
Dumoulin, Benoit",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.511/",
pages = "10506--10531",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models (LLMs) have made strong progress in reasoning. To enhance the reasoning performance, a common inference-time approach is tree-based search, which decomposes the reasoning process into multiple steps, expands multiple reasoning paths, and uses reward models to prune and select candidates. However, based on our exploration, the simple decomposition may lead to suboptimal searching efficiency: while planning is generally harder, it is the execution errors that are more likely to propagate to later steps. This indicates that planning and execution play different roles in reasoning and should be treated differently during tree-based search. Given this, to enhance the searching efficiency, we propose a dual-phase test-time scaling framework that separates reasoning into planning and execution, and performs search over each phase independently. To further refine the algorithm, we also introduce a dynamic budget allocation mechanism that adaptively redistributes sampling effort based on reward feedback, allowing early stopping on confident steps and reallocation of computation to more challenging steps. Experiments on both math reasoning and code generation benchmarks demonstrate that our approach consistently improves accuracy while reducing redundant computation."
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<abstract>Large Language Models (LLMs) have made strong progress in reasoning. To enhance the reasoning performance, a common inference-time approach is tree-based search, which decomposes the reasoning process into multiple steps, expands multiple reasoning paths, and uses reward models to prune and select candidates. However, based on our exploration, the simple decomposition may lead to suboptimal searching efficiency: while planning is generally harder, it is the execution errors that are more likely to propagate to later steps. This indicates that planning and execution play different roles in reasoning and should be treated differently during tree-based search. Given this, to enhance the searching efficiency, we propose a dual-phase test-time scaling framework that separates reasoning into planning and execution, and performs search over each phase independently. To further refine the algorithm, we also introduce a dynamic budget allocation mechanism that adaptively redistributes sampling effort based on reward feedback, allowing early stopping on confident steps and reallocation of computation to more challenging steps. Experiments on both math reasoning and code generation benchmarks demonstrate that our approach consistently improves accuracy while reducing redundant computation.</abstract>
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%0 Conference Proceedings
%T A Reward-Guided Dual-Phase Framework for Adaptive Inference-Time Reasoning
%A Cui, Yingqian
%A Dai, Zhenwei
%A He, Pengfei
%A He, Bing
%A Liu, Hui
%A Shi, Zhan
%A Tang, Xianfeng
%A Zeng, Jingying
%A Wang, Suhang
%A Xing, Yue
%A Tang, Jiliang
%A Dumoulin, Benoit
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F cui-etal-2026-reward
%X Large Language Models (LLMs) have made strong progress in reasoning. To enhance the reasoning performance, a common inference-time approach is tree-based search, which decomposes the reasoning process into multiple steps, expands multiple reasoning paths, and uses reward models to prune and select candidates. However, based on our exploration, the simple decomposition may lead to suboptimal searching efficiency: while planning is generally harder, it is the execution errors that are more likely to propagate to later steps. This indicates that planning and execution play different roles in reasoning and should be treated differently during tree-based search. Given this, to enhance the searching efficiency, we propose a dual-phase test-time scaling framework that separates reasoning into planning and execution, and performs search over each phase independently. To further refine the algorithm, we also introduce a dynamic budget allocation mechanism that adaptively redistributes sampling effort based on reward feedback, allowing early stopping on confident steps and reallocation of computation to more challenging steps. Experiments on both math reasoning and code generation benchmarks demonstrate that our approach consistently improves accuracy while reducing redundant computation.
%U https://aclanthology.org/2026.findings-acl.511/
%P 10506-10531
Markdown (Informal)
[A Reward-Guided Dual-Phase Framework for Adaptive Inference-Time Reasoning](https://aclanthology.org/2026.findings-acl.511/) (Cui et al., Findings 2026)
ACL
- Yingqian Cui, Zhenwei Dai, Pengfei He, Bing He, Hui Liu, Zhan Shi, Xianfeng Tang, Jingying Zeng, Suhang Wang, Yue Xing, Jiliang Tang, and Benoit Dumoulin. 2026. A Reward-Guided Dual-Phase Framework for Adaptive Inference-Time Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 10506–10531, San Diego, California, United States. Association for Computational Linguistics.