Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data

• URL: http://arxiv.org/abs/2601.11038v1
• Date: Fri, 16 Jan 2026 07:09:30 GMT
• Title: Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data
• Authors: Xuanming Zhang, Shwan Ashrafi, Aziza Mirsaidova, Amir Rezaeian, Miguel Ballesteros, Lydia B. Chilton, Zhou Yu, Dan Roth,
• Abstract summary: We study the reasoning behavior of large language models (LLMs) under limited computation budgets.<n>We introduce an anytime reasoning framework and the Anytime Index, a metric that quantifies how effectively solution quality improves as reasoning tokens increase.<n> Experiments on NaturalPlan (Trip), AIME, and GPQA datasets show consistent gains across Grok-3, GPT-oss, GPT-4.1/4o, and LLaMA models.
• Score: 57.996437077411315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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