100 Days After DeepSeek-R1: A Survey on Replication Studies and More
  Directions for Reasoning Language Models
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.
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Chong Zhang
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Yue Deng
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Xiang Lin
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Bin Wang
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Dianwen Ng
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dianwen ng
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dianwen
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Hai Ye
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Xingxuan Li
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Xingxuan Li
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veggiebird
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Yao Xiao
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Zhanfeng Mo
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Qi Zhang
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Lidong Bing
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Lidong Bing
user
LidongBing
100 Days After DeepSeek-R1: A Survey on Replication Studies and More
  Directions for Reasoning Language Models
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.
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.
reasoning language models RLMs DeepSeek-R1-Zero DeepSeek-R1 supervised fine-tuning SFT reinforcement learning from verifiable rewards RLVR