Iterative Deepening Sampling for Large Language Models

• URL: http://arxiv.org/abs/2502.05449v1
• Date: Sat, 08 Feb 2025 04:39:51 GMT
• Title: Iterative Deepening Sampling for Large Language Models
• Authors: Weizhe Chen, Sven Koenig, Bistra Dilkina,
• Abstract summary: Training models to achieve effective self-correction and self-correction remains a significant challenge. We propose a novel iterative sampling algorithm framework designed to enhance self-correction and generate higher-quality samples.
• Score: 27.807695570974644
• License:
• Abstract: The recent release of OpenAI's o1 models and other similar frameworks showcasing test-time scaling laws has demonstrated their exceptional capability to tackle complex reasoning tasks. Inspired by this, subsequent research has revealed that such test-time scaling laws hinge on the model's ability to search both within a single response (intra-response) and across multiple responses (inter-response) during training. Crucially, beyond selecting a single optimal response, the model must also develop robust self-correction capabilities within its own outputs. However, training models to achieve effective self-evaluation and self-correction remains a significant challenge, heavily dependent on the quality of self-reflection data. In this paper, we address this challenge by focusing on enhancing the quality of self-reflection data generation for complex problem-solving, which can subsequently improve the training of next-generation large language models (LLMs). Specifically, we explore how manually 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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