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DeepSeek-R1 is an open-source language model constructed on DeepSeek-V3-Base that's been making waves in the AI community. Not only does it match-or even surpass-OpenAI's o1 model in numerous criteria, but it likewise features completely MIT-licensed weights. This marks it as the very first non-OpenAI/Google design to deliver strong reasoning in an open and available manner.
What makes DeepSeek-R1 especially exciting is its transparency. Unlike the less-open methods from some market leaders, DeepSeek has actually released a detailed training method in their paper.
The design is also incredibly affordable, with input tokens costing just $0.14-0.55 per million (vs o1's $15) and output tokens at $2.19 per million (vs o1's $60).
Until ~ GPT-4, the common knowledge was that much better models needed more information and compute. While that's still valid, designs like o1 and R1 show an alternative: inference-time scaling through thinking.
The Essentials
The DeepSeek-R1 paper presented multiple designs, however main among them were R1 and R1-Zero. Following these are a series of distilled models that, while interesting, I won't discuss here.
DeepSeek-R1 uses 2 significant ideas:
1. A multi-stage pipeline where a small set of cold-start data kickstarts the design, followed by massive RL.
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