ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning
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Computer Science > Computation and Language
arXiv:2606.06915 (cs)
[Submitted on 5 Jun 2026]
Title:ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning
Authors:Vladislav Smirnov (1), Chieu Nguyen (1), Sergey Senichev (7), Minh Ngoc Ta (1), Ekaterina Fadeeva (2), Artem Vazhentsev (1), Daria Galimzianova (1), Nikolai Rozanov (1 and 3), Viktor Mazanov (6), Jingwei Ni (2), Tianyi Wu (4), Igor Kiselev (5), Mrinmaya Sachan (2), Iryna Gurevych (1), Preslav Nakov (1), Timothy Baldwin (1), Artem Shelmanov (1) ((1) MBZUAI, (2) ETH Zürich, (3) Imperial College London, (4) NUS, (5) Accenture, (6) Innopolis University, (7) Independent Researcher)
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Abstract:Test-time compute (TTC) scaling has emerged as a powerful paradigm for improving large language model (LLM) reasoning by allocating additional compute during inference, e.g., via multi-sample generation and verifier-based reranking. Existing TTC scaling strategies and reasoning scorers remain fragmented, evaluated under inconsistent protocols, and are rarely analyzed through the lens of quality-cost trade-offs. We introduce ThinkBooster, a unified framework for seamless test-time compute scaling of LLM reasoning, which consists of (i) a modular Python library implementing state-of-the-art TTC scaling strategy and scorer families, (ii) a benchmark that jointly evaluates performance and computational efficiency, and (iii) a deployable OpenAI-compatible proxy service that enables drop-in integration of adaptive reasoning into real-world applications. We further provide a demo visual debugger for inspecting the reasoning trajectories, intermediate selection decisions, and alternative reasoning paths. Empirical results on mathematical and coding tasks reveal the performance-compute trade-offs of TTC scaling strategies and scoring methods and demonstrate that ThinkBooster provides practical gains in real-world tasks. The code is available online under an MIT license.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.06915 [cs.CL] |
| (or arXiv:2606.06915v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.06915
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Vladislav Smirnov Mr. [view email][v1] Fri, 5 Jun 2026 05:28:46 UTC (1,180 KB)
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