Iterative Deepening Sampling as Efficient Test-Time Scaling

• URL: http://arxiv.org/abs/2502.05449v2
• Date: Sun, 01 Jun 2025 20:14:16 GMT
• Title: Iterative Deepening Sampling as Efficient Test-Time Scaling
• Authors: Weizhe Chen, Sven Koenig, Bistra Dilkina,
• Abstract summary: Recent reasoning models, such as OpenAI's O1 series, have demonstrated exceptional performance on complex reasoning tasks.<n>We propose a novel iterative deepening sampling algorithm framework designed to enhance self-correction and generate higher-quality samples.
• Score: 27.807695570974644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent reasoning models, such as OpenAI's O1 series, have demonstrated exceptional performance on complex reasoning tasks and revealed new test-time scaling laws. Inspired by this, many people have been studying how to train models to achieve effective self-evaluation and self-correction to further enable the scaling paradigm. However, less studied is how to efficiently scale test-time compute from a fixed model, and this remains a challenge. In this paper, we address this challenge by focusing on enhancing the quality of self-reflection data generation for complex problem-solving at test time, which can also subsequently improve the training of next-generation large language models (LLMs). Specifically, we explore how systematically triggering a model's self-correction mechanisms can improve performance on challenging reasoning tasks. To this end, we propose a novel iterative deepening sampling algorithm framework designed to enhance self-correction and generate higher-quality samples. Through extensive experiments on Math500 and AIME benchmarks, we demonstrate that our method achieves a higher success rate on difficult tasks and provide detailed ablation studies to analyze its effectiveness across diverse settings.

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