title: The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT
    Distillation
summary: 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 at https://huggingface.co/datasets/rana-shahroz/DC-COT, while our code is
  shared in https://anonymous.4open.science/r/DC-COT-FF4C/.
paper_authors._id: 683551c54f3166e8677b43bb
paper_authors.name: Ruichen Zhang
paper_authors._id: 683551c54f3166e8677b43bc
paper_authors.name: Rana Muhammad Shahroz Khan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:44:03.117Z
paper_authors._id: 66c3c6362773cfcc33f214ed
paper_authors.avatarUrl: /avatars/f4d99a4165208543f9f4d4f9055b9c6f.svg
paper_authors.fullname: Rana Muhammad Shahroz Khan
paper_authors.type: user
paper_authors.user: rana-shahroz
paper_authors._id: 683551c54f3166e8677b43bd
paper_authors.name: Zhen Tan
paper_authors._id: 683551c54f3166e8677b43be
paper_authors.name: Dawei Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:49:14.749Z
paper_authors._id: 6474e1afb68461d5cf7c41cc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6474e1afb68461d5cf7c41cc/bcoiD_qPrjHUBlB259djg.png
paper_authors.fullname: Dawei Li
paper_authors.type: user
paper_authors.user: wjldw
paper_authors._id: 683551c54f3166e8677b43bf
paper_authors.name: Song Wang
paper_authors._id: 683551c54f3166e8677b43c0
paper_authors.name: Tianlong Chen
paper_title: The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT
    Distillation
paper_summary: 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 at https://huggingface.co/datasets/rana-shahroz/DC-COT, while our code is
  shared in https://anonymous.4open.science/r/DC-COT-FF4C/.
paper_ai_summary: DC-CoT provides a comprehensive benchmark for assessing data-centric distillation techniques in chain-of-thought distillation, focusing on performance and generalization across different models and datasets.
paper_ai_keywords: data-centric distillation data augmentation data selection data mixing chain-of-thought (CoT) in-distribution (IID) out-of-distribution (OOD) cross-domain transfer