Token-Budget-Aware LLM Reasoning
The paper introduces a token-budget-aware framework for large language model reasoning that dynamically limits reasoning token usage. Experiments show the method reduces token costs in chain-of-thought reasoning with only minor performance loss. The approach aims to balance efficiency and accuracy in LLM tasks.
- ▪Current chain-of-thought reasoning improves performance but significantly increases token consumption.
- ▪The authors observe that reasoning steps can be compressed by specifying a token budget in the prompt.
- ▪Their proposed framework adjusts the number of reasoning tokens based on problem complexity.
- ▪Evaluation demonstrates notable token cost reductions with only slight drops in accuracy.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2412.18547 |
| Publication time | Thu, 06 Aug 2026 06:17:50 +0000 |
| Retrieval time | 2026-08-06T06:25:47.576Z |
| Last seen | 2026-08-06T06:25:47.576Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | UcPcVYbuU5dT · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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 View a PDF of the paper titled Token-Budget-Aware LLM Reasoning, by Tingxu Han and 5 other authors View PDF HTML (experimental) 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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