[2412.18547] Token-Budget-Aware LLM Reasoning

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Computer Science > Computation and Language

arXiv:2412.18547 (cs)

[Submitted on 24 Dec 2024 (v1), last revised 2 Jun 2025 (this version, v5)]

Title:Token-Budget-Aware LLM Reasoning

Authors:Tingxu Han, Zhenting Wang, Chunrong Fang, Shiyu Zhao, Shiqing Ma, Zhenyu Chen<br>View a PDF of the paper titled Token-Budget-Aware LLM Reasoning, by Tingxu Han and 5 other authors

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Abstract:Reasoning is critical for large language models (LLMs) to excel in a wide range of tasks. While methods like Chain-of-Thought (CoT) reasoning and enhance LLM performance by decomposing problems into intermediate steps, they also incur significant overhead in token usage, leading to increased costs. We find that the reasoning process of current LLMs is unnecessarily lengthy and it can be compressed by including a reasonable token budget in the prompt, but the choice of token budget plays a crucial role in the actual compression effectiveness. We then propose a token-budget-aware LLM reasoning framework that dynamically adjusts the number of reasoning tokens based on the reasoning complexity of each problem. Experiments show that our method effectively reduces token costs in CoT reasoning with only a slight performance reduction, offering a practical solution to balance efficiency and accuracy in LLM reasoning. Code: this https URL

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as:<br>arXiv:2412.18547 [cs.CL]

(or<br>arXiv:2412.18547v5 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2412.18547

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arXiv-issued DOI via DataCite

Submission history<br>From: Tingxu Han [view email]<br>[v1]<br>Tue, 24 Dec 2024 16:55:45 UTC (533 KB)

[v2]<br>Mon, 30 Dec 2024 01:07:39 UTC (533 KB)

[v3]<br>Tue, 31 Dec 2024 06:11:39 UTC (533 KB)

[v4]<br>Mon, 17 Feb 2025 15:55:08 UTC (584 KB)

[v5]<br>Mon, 2 Jun 2025 00:44:09 UTC (666 KB)

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