@inproceedings{smirnov-etal-2026-thinkbooster,
title = "{T}hink{B}ooster: A Unified Framework for Seamless Test-Time Scaling of {LLM} Reasoning",
author = "Smirnov, Vladislav and
Nguyen, Quang-Chieu and
Senichev, Sergey and
Ta, Minh Ngoc and
Fadeeva, Ekaterina and
Vazhentsev, Artem and
Galimzianova, Daria and
Rozanov, Nikolai and
Mazanov, Viktor and
Ni, Jingwei and
Wu, Tianyi and
Kiselev, Igor and
Sachan, Mrinmaya and
Gurevych, Iryna and
Nakov, Preslav and
Baldwin, Timothy and
Shelmanov, Artem",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.70/",
pages = "715--727",
ISBN = "979-8-89176-392-0",
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."
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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.</abstract>
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%0 Conference Proceedings
%T ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning
%A Smirnov, Vladislav
%A Nguyen, Quang-Chieu
%A Senichev, Sergey
%A Ta, Minh Ngoc
%A Fadeeva, Ekaterina
%A Vazhentsev, Artem
%A Galimzianova, Daria
%A Rozanov, Nikolai
%A Mazanov, Viktor
%A Ni, Jingwei
%A Wu, Tianyi
%A Kiselev, Igor
%A Sachan, Mrinmaya
%A Gurevych, Iryna
%A Nakov, Preslav
%A Baldwin, Timothy
%A Shelmanov, Artem
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F smirnov-etal-2026-thinkbooster
%X 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.
%U https://aclanthology.org/2026.acl-demo.70/
%P 715-727
Markdown (Informal)
[ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning](https://aclanthology.org/2026.acl-demo.70/) (Smirnov et al., ACL 2026)
ACL
- Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, Minh Ngoc Ta, Ekaterina Fadeeva, Artem Vazhentsev, Daria Galimzianova, Nikolai Rozanov, Viktor Mazanov, Jingwei Ni, Tianyi Wu, Igor Kiselev, Mrinmaya Sachan, Iryna Gurevych, Preslav Nakov, Timothy Baldwin, and Artem Shelmanov. 2026. ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 715–727, San Diego, California, United States. Association for Computational Linguistics.