DeepSeek-R1: Models that boost reasoning and now open-source
A new set of small to giant models called DeepSeek-R1-Zero and DeepSeek-R1 aims to make computers think through problems, not just copy answers.
The first, DeepSeek-R1-Zero, learned by playing with rewards and shows surprising reasoning skills, sometimes finding clever steps humans miss.
But its writing could be messy, it might mix languages or phrasing.
To fix that, the team trained a second version with staged learning, adding a gentle start before reward stage.
That helped accuracy and readability, bringing results closer to top systems.
The project used reinforcement learning to shape behavior and then made smaller versions for faster use.
Everything is open-source, so researchers and hobbyists can try them, tweak and build new tools.
This means more people can test thinking models, notice quirks, and help make them safer and useful.
Try it, explore results, and see what surprises pop up, you may find something unexpected.
Read article comprehensive review in Paperium.net:
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via ReinforcementLearning
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