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

ACL ARR 2025 May Submission2815 Authors

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.

Paper Type: Long

Research Area: Efficient/Low-Resource Methods for NLP

Research Area Keywords: Language Modeling, Efficient/Low-Resource Methods for NLP, Generation, fine-tuning, scaling

Contribution Types: Approaches to low-resource settings, Approaches low compute settings-efficiency, Surveys

Languages Studied: English

Submission Number: 2815