Title: Double-Checker: Enhancing Reasoning of Slow-Thinking LLMs via Self-Critical Fine-Tuning
Primary area: foundation or frontier models, including LLMs
Keywords: Large Language Models, Reasoning, Long Chain-of-Thought, Critique

Abstract: While slow-thinking large language models (LLMs) exhibit reflection-like reasoning, commonly referred to as the “aha moment”, their ability to generate informative critiques and refine prior solutions remains limited. In this paper, we introduce **Double-Checker**, a principled framework designed to enhance the reasoning capabilities of slow-thinking LLMs by fostering explicit self-critique and iterative refinement of their previous solutions. By fine-tuning on our curated 1,730 self-critical instances, *Double-Checker* empowers long-CoT LLMs to iteratively critique and refine their outputs during inference until their solutions are evaluated as correct under self-generated critiques.
We validate the efficacy of *Double-Checker* across various reasoning benchmarks, demonstrating that iterative self-critique significantly enhances the reasoning capabilities of long-CoT LLMs. Notably, our Double-Checker increases the pass@1 performance on challenging AIME benchmarks from 4.4% to 18.2% compared to the original long-CoT LLMs. These results highlight a promising direction for developing more trustworthy and effective LLMs capable of structured self-critique.

TL;DR: We introduce Double-Checker, a principled framework designed to enhance the reasoning capabilities of slow-thinking LLMs by fostering explicit self-critique and iterative refinement of their previous solutions.