ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning

• URL: http://arxiv.org/abs/2606.06915v2
• Date: Mon, 08 Jun 2026 11:00:40 GMT
• Title: ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning
• Abstract summary: ThinkBooster is a framework for seamless test-time compute scaling of large language model (LLM) reasoning.<n>It consists of a modular Python library implementing state-of-the-art TTC scaling strategy and scorer families.<n>A deployable OpenAI-compatible proxy service enables drop-in integration of adaptive reasoning into real-world applications.
• Score: 110.46431027868954
• License: http://creativecommons.org/licenses/by/4.0/
• 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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