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

• URL: http://arxiv.org/abs/2506.04611v1
• Date: Thu, 05 Jun 2025 04:02:17 GMT
• Title: Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning
• Authors: Ho-Lam Chung, Teng-Yun Hsiao, Hsiao-Ying Huang, Chunerh Cho, Jian-Ren Lin, Zhang Ziwei, Yun-Nung Chen,
• Abstract summary: Test-Time Scaling (TTS) improves the reasoning performance of Large Language Models (LLMs) by allocating additional compute during inference.<n>We observe that reasoning-optimized models often produce less diverse outputs, which limits TTS effectiveness.<n>We propose ADAPT, a lightweight method that applies prefix tuning with a diversity-focused data strategy.
• Score: 19.426979775180797
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Scaling Test-time Compute for LLM Agents [51.790752085445384]
  Scaling test time compute has shown remarkable success in improving the reasoning abilities of large language models (LLMs)<n>In this work, we conduct the first systematic exploration of applying test-time scaling methods to language agents.
  arXiv  Detail & Related papers  (2025-06-15T17:59:47Z)
• Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs' Reasoning [81.50681925980135]
  We propose Stepwise Reasoning Checkpoint Analysis (SRCA), a framework that introduces checkpoints between reasoning steps.<n>It incorporates two key strategies: (1) Answer-Clustered Search, which groups reasoning paths by their intermediate checkpoint answers to maintain diversity while ensuring quality, and (2) Checkpoint Candidate Augmentation, which leverages all intermediate answers for final decision-making.<n>Our approach effectively reduces path homogenization and creates a fault-tolerant mechanism by utilizing high-quality intermediate results.
  arXiv  Detail & Related papers  (2025-05-23T12:42:50Z)
• Test-Time Training Provably Improves Transformers as In-context Learners [49.09821664572445]
  We investigate a gradient-based TTT algorithm for in-context learning.<n>We train a transformer model on the in-context demonstrations provided in the test prompt.<n>As our empirical contribution, we study the benefits of TTT for TabPFN.
  arXiv  Detail & Related papers  (2025-03-14T20:06:37Z)
• Diversified Sampling Improves Scaling LLM inference [31.18762591875725]
  DivSampling is a novel and versatile sampling technique designed to enhance the diversity of candidate solutions.<n>Our theoretical analysis demonstrates that, under mild assumptions, the error rates of responses generated from diverse prompts are significantly lower compared to those produced by stationary prompts.
  arXiv  Detail & Related papers  (2025-02-16T07:37:58Z)
• The Surprising Effectiveness of Test-Time Training for Few-Shot Learning [59.309477460893916]
  Language models (LMs) have shown impressive performance on tasks within their training distribution, but often struggle with structurally novel tasks.<n>We investigate the effectiveness of test-time training (TTT) as a mechanism for improving LMs' reasoning and few-shot learning capabilities.<n>Our findings highlight the limitations of in-context learning for novel tasks and demonstrate the potential of test-time training to enhance language model adaptability.
  arXiv  Detail & Related papers  (2024-11-11T18:59:45Z)
• Think Beyond Size: Adaptive Prompting for More Effective Reasoning [0.0]
  We introduce Adaptive Prompting, a dynamic and iterative framework designed to enhance reasoning by incorporating real-time adjustments to prompt structures and validation mechanisms.<n>Results demonstrate that Adaptive Prompting significantly improves performance on diverse reasoning benchmarks, including arithmetic reasoning (GSM8K, MultiArithm), logical reasoning and commonsense tasks.<n>Our approach enables smaller models to achieve competitive performance with larger counterparts, such as GPT-4, while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2024-10-10T17:14:36Z)
• DATTA: Towards Diversity Adaptive Test-Time Adaptation in Dynamic Wild World [6.816521410643928]
  This paper proposes a new general method, named Diversity Adaptive Test-Time Adaptation (DATTA), aimed at improving Quality of Experience (QoE) It features three key components: Diversity Discrimination (DD) to assess batch diversity, Diversity Adaptive Batch Normalization (DABN) to tailor normalization methods based on DD insights, and Diversity Adaptive Fine-Tuning (DAFT) to selectively fine-tune the model. Experimental results show that our method achieves up to a 21% increase in accuracy compared to state-of-the-art methodologies.
  arXiv  Detail & Related papers  (2024-08-15T09:50:11Z)
• Active Test-Time Adaptation: Theoretical Analyses and An Algorithm [51.84691955495693]
  Test-time adaptation (TTA) addresses distribution shifts for streaming test data in unsupervised settings. We propose the novel problem setting of active test-time adaptation (ATTA) that integrates active learning within the fully TTA setting.
  arXiv  Detail & Related papers  (2024-04-07T22:31:34Z)
• Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning [73.75282761503581]
  We propose DiffTPT, which leverages pre-trained diffusion models to generate diverse and informative new data. Our experiments on test datasets with distribution shifts and unseen categories demonstrate that DiffTPT improves the zero-shot accuracy by an average of 5.13%.
  arXiv  Detail & Related papers  (2023-08-11T09:36:31Z)
• Improved Test-Time Adaptation for Domain Generalization [48.239665441875374]
  Test-time training (TTT) adapts the learned model with test data. This work addresses two main factors: selecting an appropriate auxiliary TTT task for updating and identifying reliable parameters to update during the test phase. We introduce additional adaptive parameters for the trained model, and we suggest only updating the adaptive parameters during the test phase.
  arXiv  Detail & Related papers  (2023-04-10T10:12:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.