@inproceedings{zhang-etal-2026-budget,
title = "Budget-Aware Anytime Reasoning with {LLM}-Synthesized Preference Data",
author = "Zhang, Xuanming and
Ashrafi, Shwan and
Mirsaidova, Aziza and
Rezaeian, Amir H. and
Ballesteros, Miguel and
Chilton, Lydia and
Yu, Zhou and
Roth, Dan",
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.417/",
pages = "8587--8599",
ISBN = "979-8-89176-395-1",
abstract = "We study the reasoning behavior of large language models (LLMs) under limited computation budgets. In such settings, producing useful partial solutions quickly is often more practical than exhaustive reasoning, which incurs high inference costs. Many real-world tasks, such as trip planning, require models to deliver the best possible output within a fixed reasoning budget. We introduce an anytime reasoning framework and the Anytime Index, a metric that quantifies how effectively solution quality improves as reasoning tokens increase. To further enhance efficiency, we propose an inference-time self-improvement method using LLM-synthesized preference data, where models learn from their own reasoning comparisons to produce better intermediate solutions. Experiments on NaturalPlan (Trip), AIME, and GPQA datasets show consistent gains across Grok-3, GPT-oss, GPT-4.1/4o, and LLaMA models, improving both reasoning quality and efficiency under budget constraints."
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<abstract>We study the reasoning behavior of large language models (LLMs) under limited computation budgets. In such settings, producing useful partial solutions quickly is often more practical than exhaustive reasoning, which incurs high inference costs. Many real-world tasks, such as trip planning, require models to deliver the best possible output within a fixed reasoning budget. We introduce an anytime reasoning framework and the Anytime Index, a metric that quantifies how effectively solution quality improves as reasoning tokens increase. To further enhance efficiency, we propose an inference-time self-improvement method using LLM-synthesized preference data, where models learn from their own reasoning comparisons to produce better intermediate solutions. Experiments on NaturalPlan (Trip), AIME, and GPQA datasets show consistent gains across Grok-3, GPT-oss, GPT-4.1/4o, and LLaMA models, improving both reasoning quality and efficiency under budget constraints.</abstract>
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%0 Conference Proceedings
%T Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data
%A Zhang, Xuanming
%A Ashrafi, Shwan
%A Mirsaidova, Aziza
%A Rezaeian, Amir H.
%A Ballesteros, Miguel
%A Chilton, Lydia
%A Yu, Zhou
%A Roth, Dan
%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 zhang-etal-2026-budget
%X We study the reasoning behavior of large language models (LLMs) under limited computation budgets. In such settings, producing useful partial solutions quickly is often more practical than exhaustive reasoning, which incurs high inference costs. Many real-world tasks, such as trip planning, require models to deliver the best possible output within a fixed reasoning budget. We introduce an anytime reasoning framework and the Anytime Index, a metric that quantifies how effectively solution quality improves as reasoning tokens increase. To further enhance efficiency, we propose an inference-time self-improvement method using LLM-synthesized preference data, where models learn from their own reasoning comparisons to produce better intermediate solutions. Experiments on NaturalPlan (Trip), AIME, and GPQA datasets show consistent gains across Grok-3, GPT-oss, GPT-4.1/4o, and LLaMA models, improving both reasoning quality and efficiency under budget constraints.
%U https://aclanthology.org/2026.findings-acl.417/
%P 8587-8599
Markdown (Informal)
[Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data](https://aclanthology.org/2026.findings-acl.417/) (Zhang et al., Findings 2026)
ACL
- Xuanming Zhang, Shwan Ashrafi, Aziza Mirsaidova, Amir H. Rezaeian, Miguel Ballesteros, Lydia Chilton, Zhou Yu, and Dan Roth. 2026. Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data. In Findings of the Association for Computational Linguistics: ACL 2026, pages 8587–8599, San Diego, California, United States. Association for Computational Linguistics.