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arXiv · 2506.04611

Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning

Ho-Lam Chung·Teng-Yun Hsiao·Hsiao-Ying Huang·Chunerh Cho·Jian-Ren Lin·Zhang Ziwei·Yun-Nung Chen
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Abstract

Test-Time Scaling (TTS) improves the reasoning performance of Large Language Models (LLMs) by allocating additional compute during inference. We conduct a structured survey of TTS methods and categorize them into sampling-based, search-based, and trajectory optimization strategies. We observe that reasoning-optimized models often produce less diverse outputs, which limits TTS effectiveness. To address this, we propose ADAPT (A Diversity Aware Prefix fine-Tuning), a lightweight method that applies prefix tuning with a diversity-focused data strategy. Experiments on mathematical reasoning tasks show that ADAPT reaches 80% accuracy using eight times less compute than strong baselines. Our findings highlight the essential role of generative diversity in maximizing TTS effectiveness.

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Ho-Lam Chung, Teng-Yun Hsiao, Hsiao-Ying Huang, Chunerh Cho, Jian-Ren Lin, Zhang Ziwei, Yun-Nung Chen. 2025-06-05. Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning. https://arxiv.org/abs/2506.04611

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