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.
@article{arxiv.2506.04611,
title = {Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning},
author = {Ho-Lam Chung and Teng-Yun Hsiao and Hsiao-Ying Huang and Chunerh Cho and Jian-Ren Lin and Zhang Ziwei and Yun-Nung Chen},
journal= {arXiv preprint arXiv:2506.04611},
year = {2025}
}