SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling

• URL: http://arxiv.org/abs/2501.19306v4
• Date: Sat, 30 Aug 2025 05:12:37 GMT
• Title: SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling
• Authors: Jiefeng Chen, Jie Ren, Xinyun Chen, Chengrun Yang, Ruoxi Sun, Jinsung Yoon, Sercan Ö Arık,
• Abstract summary: Self-Enhanced Test-Time Scaling (SETS) is a simple yet effective approach that overcomes limitations by strategically combining parallel and sequential techniques.<n>SETS exploits the inherent self-verification and self- computation capabilities of Large Language Models, unifying sampling, verification, and correction within a single framework.<n>Our results show SETS achieves significant performance improvements and more advantageous test-time scaling behavior than the alternatives.
• Score: 39.57154199908565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in Large Language Models (LLMs) have created new opportunities to enhance performance on complex reasoning tasks by leveraging test-time computation. However, existing scaling methods have key limitations: parallel methods like repeated sampling are often inefficient and quickly saturate, while sequential methods like SELF-REFINE struggle to improve after a few rounds. Although combining these approaches shows promise, current methods require fine-tuned reward and revision models. This paper proposes Self-Enhanced Test-Time Scaling (SETS), a simple yet effective approach that overcomes these limitations by strategically combining parallel and sequential techniques and fully leveraging LLMs' self-improvement abilities. SETS exploits the inherent self-verification and self-correction capabilities of LLMs, unifying sampling, verification, and correction within a single framework. This facilitates efficient and scalable test-time computation for enhanced performance on complex tasks without any model training. Our comprehensive experimental results on challenging benchmarks spanning planning, reasoning, math, and coding demonstrate that SETS achieves significant performance improvements and more advantageous test-time scaling behavior than the alternatives.

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