title: 100 Days After DeepSeek-R1: A Survey on Replication Studies and More
    Directions for Reasoning Language Models
summary: The recent development of reasoning language models (RLMs) represents a novel
  evolution in large language models. In particular, the recent release of
  DeepSeek-R1 has generated widespread social impact and sparked enthusiasm in
  the research community for exploring the explicit reasoning paradigm of
  language models. However, the implementation details of the released models
  have not been fully open-sourced by DeepSeek, including DeepSeek-R1-Zero,
  DeepSeek-R1, and the distilled small models. As a result, many replication
  studies have emerged aiming to reproduce the strong performance achieved by
  DeepSeek-R1, reaching comparable performance through similar training
  procedures and fully open-source data resources. These works have investigated
  feasible strategies for supervised fine-tuning (SFT) and reinforcement learning
  from verifiable rewards (RLVR), focusing on data preparation and method design,
  yielding various valuable insights. In this report, we provide a summary of
  recent replication studies to inspire future research. We primarily focus on
  SFT and RLVR as two main directions, introducing the details for data
  construction, method design and training procedure of current replication
  studies. Moreover, we conclude key findings from the implementation details and
  experimental results reported by these studies, anticipating to inspire future
  research. We also discuss additional techniques of enhancing RLMs, highlighting
  the potential of expanding the application scope of these models, and
  discussing the challenges in development. By this survey, we aim to help
  researchers and developers of RLMs stay updated with the latest advancements,
  and seek to inspire new ideas to further enhance RLMs.
paper_authors._id: 68141e6ebaa634cf71cde5e9
paper_authors.name: Chong Zhang
paper_authors._id: 68141e6ebaa634cf71cde5ea
paper_authors.name: Yue Deng
paper_authors._id: 68141e6ebaa634cf71cde5eb
paper_authors.name: Xiang Lin
paper_authors._id: 68141e6ebaa634cf71cde5ec
paper_authors.name: Bin Wang
paper_authors._id: 68141e6ebaa634cf71cde5ed
paper_authors.name: Dianwen Ng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:58:55.945Z
paper_authors._id: 67443af53681b74e07c6ee45
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/RF8YcS2KdxOfIbNznYVx3.png
paper_authors.fullname: dianwen ng
paper_authors.type: user
paper_authors.user: dianwen
paper_authors._id: 68141e6ebaa634cf71cde5ee
paper_authors.name: Hai Ye
paper_authors._id: 68141e6ebaa634cf71cde5ef
paper_authors.name: Xingxuan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:59:02.519Z
paper_authors._id: 6362a77dd3be91534c2e9213
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paper_authors.fullname: Xingxuan Li
paper_authors.type: user
paper_authors.user: veggiebird
paper_authors._id: 68141e6ebaa634cf71cde5f0
paper_authors.name: Yao Xiao
paper_authors._id: 68141e6ebaa634cf71cde5f1
paper_authors.name: Zhanfeng Mo
paper_authors._id: 68141e6ebaa634cf71cde5f2
paper_authors.name: Qi Zhang
paper_authors._id: 68141e6ebaa634cf71cde5f3
paper_authors.name: Lidong Bing
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-02T06:34:34.315Z
paper_authors._id: 6454685a548f22be598414c4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eMjMWKJ-AouF7eY1-RzGF.jpeg
paper_authors.fullname: Lidong Bing
paper_authors.type: user
paper_authors.user: LidongBing
paper_title: 100 Days After DeepSeek-R1: A Survey on Replication Studies and More
    Directions for Reasoning Language Models
paper_summary: The recent development of reasoning language models (RLMs) represents a novel
  evolution in large language models. In particular, the recent release of
  DeepSeek-R1 has generated widespread social impact and sparked enthusiasm in
  the research community for exploring the explicit reasoning paradigm of
  language models. However, the implementation details of the released models
  have not been fully open-sourced by DeepSeek, including DeepSeek-R1-Zero,
  DeepSeek-R1, and the distilled small models. As a result, many replication
  studies have emerged aiming to reproduce the strong performance achieved by
  DeepSeek-R1, reaching comparable performance through similar training
  procedures and fully open-source data resources. These works have investigated
  feasible strategies for supervised fine-tuning (SFT) and reinforcement learning
  from verifiable rewards (RLVR), focusing on data preparation and method design,
  yielding various valuable insights. In this report, we provide a summary of
  recent replication studies to inspire future research. We primarily focus on
  SFT and RLVR as two main directions, introducing the details for data
  construction, method design and training procedure of current replication
  studies. Moreover, we conclude key findings from the implementation details and
  experimental results reported by these studies, anticipating to inspire future
  research. We also discuss additional techniques of enhancing RLMs, highlighting
  the potential of expanding the application scope of these models, and
  discussing the challenges in development. By this survey, we aim to help
  researchers and developers of RLMs stay updated with the latest advancements,
  and seek to inspire new ideas to further enhance RLMs.
paper_ai_summary: Recent replication studies explore the supervised fine-tuning and reinforcement learning from verifiable rewards of reasoning language models, focusing on data preparation and method design, and highlight challenges and potential enhancements.
paper_ai_keywords: reasoning language models RLMs DeepSeek-R1-Zero DeepSeek-R1 supervised fine-tuning SFT reinforcement learning from verifiable rewards RLVR