What This Documents
DeepSeek published a detailed technical paper for DeepSeek-R1 (early 2025), describing a reinforcement-learning-based approach to training reasoning capability in large language models, which drew substantial attention in the research community for both its methodology and its open release.
What's Publicly Known
The paper describes training an initial model (referred to as DeepSeek-R1-Zero) using large-scale reinforcement learning without a supervised fine-tuning stage first, demonstrating that meaningful reasoning capability could emerge from RL training alone. The full DeepSeek-R1 model then combined this RL approach with additional training stages to improve readability and general usability.
DeepSeek released the model with open weights alongside the detailed paper, and also released a set of smaller distilled models based on the same reasoning approach, which contributed to the paper's wide discussion and adoption in the broader research and developer community.
Verify Current Details
Treat the summary above as a general orientation rather than a substitute for the primary source — for exact technical details, benchmark figures, and methodology, read DeepSeek's own published documentation directly.
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Frequently Asked
What makes DeepSeek-R1 notable?
Its reinforcement-learning-based approach to training reasoning capability, published in detail and released with open weights, which drew substantial attention for both the methodology and openness.
What is DeepSeek-R1-Zero?
An initial model in the same published research trained via large-scale RL without a supervised fine-tuning stage first, showing reasoning capability could emerge from RL training alone.
Was DeepSeek-R1 released with open weights?
Yes, along with distilled smaller models based on the same approach.
Where can I read the original paper?
Check DeepSeek's official publications and arXiv for the original technical report.