The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT Distillation

Data-centric distillation, including data augmentation, selection, and mixing, offers a promising path to creating smaller, more efficient student Large Language Models (LLMs) that retain strong reasoning abilities. However, there still lacks a comprehensive benchmark to systematically assess the effect of each distillation approach. This paper introduces DC-CoT, the first data-centric benchmark that investigates data manipulation in chain-of-thought (CoT) distillation from method, model and data perspectives. Utilizing various teacher models (e.g., o4-mini, Gemini-Pro, Claude-3.5) and student architectures (e.g., 3B, 7B parameters), we rigorously evaluate the impact of these data manipulations on student model performance across multiple reasoning datasets, with a focus on in-distribution (IID) and out-of-distribution (OOD) generalization, and cross-domain transfer. Our findings aim to provide actionable insights and establish best practices for optimizing CoT distillation through data-centric techniques, ultimately facilitating the development of more accessible and capable reasoning models. The dataset can be found atthis https URL, while our code is shared inthis https URL.

@article{zhang2025_2505.18759,
  title={ The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT Distillation },
  author={ Ruichen Zhang and Rana Muhammad Shahroz Khan and Zhen Tan and Dawei Li and Song Wang and Tianlong Chen },
  journal={arXiv preprint arXiv:2505.18759},
  year={ 2025 }
}