The Sequence Research #490: A Practical Deep Dive Inside DeepSeek-R1
A pragmatic view into the techniques that contributed to R1's incredible performance.
I have been waiting a few days until the madness around DeepSeek-R1 slowed down to discuss some of the details around this model. Yesterday, we debated the GPU optimization techniques used in the model and today we would like to dive into the R1 model itself discussing some of its key contribitions.
Quite often we see releases in generative AI that truly challenges people’s imagination. This is the DeepSeek-R1, the newest model by the famous Chinese eval lab that dabbles into reasoning. One of the dominat reasoning thesis in the market is that it’s an emerging property of the scaling laws. In other words, you need big models to get reasoning. DeepSeek-R1 challenges that thesis achieving reasoning by leveraging a very clever post-training process. The model is able to match the performance of GPT-o1 at a fraction of the compute cost. Quite amazing.
Let’s dive in: