It cost so little because all previous open source work was already done, and a lot of the research work had already been knocked out. Building models isn't the time consuming process it used to be, it's the training, testing, retraining loop that's expensive.

If you're just building a model that is focused on specific things-like coding, math, and logic-then you don't need large swathes of content from the internet, you can just train it on already solved, freely available information. If you want to piss away money on an LLM that also knows how many celebrities each celebrity has diddled, well that costs a lot more to make.

Even if that was true, it's fair game. After all the OpenAI models etc. are entirely based on stolen content as well.

From someone in the field

It lowered training costs by quite a bit. To learn from preference data (whats termed as alignment with human values), we used a very large reward model as a proxy for human feedback. 

They completely got rid of this, hence also the need to have very large clusters

This has serious implications for spending though. Big companies who would have to train foundation models coz they couldnt directly use meta's llama, can now just use deepseek.

and directly move to the human/customer alignment phase, which was already  significantly cheaper than pretraining (first phase of foundation model training). With their new algorithm, even the later stage does not need huge compute

so they def got rid of a big chunk of compute by not relying on what is called a “reward” model

GRPO: group relative policy optimization

huggingface is trying to replicate their results

https://github.com/huggingface/open-r1