Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910989122797568 |
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| author | Chung, Ho-Lam Hsiao, Teng-Yun Huang, Hsiao-Ying Cho, Chunerh Lin, Jian-Ren Ziwei, Zhang Chen, Yun-Nung |
| author_facet | Chung, Ho-Lam Hsiao, Teng-Yun Huang, Hsiao-Ying Cho, Chunerh Lin, Jian-Ren Ziwei, Zhang Chen, Yun-Nung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04611 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning Chung, Ho-Lam Hsiao, Teng-Yun Huang, Hsiao-Ying Cho, Chunerh Lin, Jian-Ren Ziwei, Zhang Chen, Yun-Nung Computation and Language 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. |
| title | Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.04611 |