There’s extra. To make its use of reinforcement studying as environment friendly as attainable, DeepSeek has additionally developed a brand new algorithm known as Group Relative Coverage Optimization (GRPO). It first used GRPO a yr in the past, to construct a mannequin known as DeepSeekMath.
We’ll skip the details—you simply must know that reinforcement studying includes calculating a rating to find out whether or not a possible transfer is sweet or dangerous. Many present reinforcement-learning methods require an entire separate mannequin to make this calculation. Within the case of huge language fashions, meaning a second mannequin that may very well be as costly to construct and run as the primary. As an alternative of utilizing a second mannequin to foretell a rating, GRPO simply makes an informed guess. It’s low cost, however nonetheless correct sufficient to work.
A standard method
DeepSeek’s use of reinforcement studying is the principle innovation that the corporate describes in its R1 paper. However DeepSeek will not be the one agency experimenting with this system. Two weeks earlier than R1 dropped, a staff at Microsoft Asia introduced a mannequin known as rStar-Math, which was skilled in an analogous means. “It has equally large leaps in efficiency,” says Matt Zeiler, founder and CEO of the AI agency Clarifai.
AI2’s Tulu was additionally constructed utilizing environment friendly reinforcement-learning methods (however on prime of, not as an alternative of, human-led steps like supervised fine-tuning and RLHF). And the US agency Hugging Face is racing to copy R1 with OpenR1, a clone of DeepSeek’s mannequin that Hugging Face hopes will expose much more of the substances in R1’s particular sauce.
What’s extra, it’s an open secret that prime corporations like OpenAI, Google DeepMind, and Anthropic might already be utilizing their very own variations of DeepSeek’s method to coach their new era of fashions. “I’m positive they’re doing virtually the very same factor, however they’ll have their very own taste of it,” says Zeiler.
However DeepSeek has a couple of trick up its sleeve. It skilled its base mannequin V3 to do one thing known as multi-token prediction, the place the mannequin learns to foretell a string of phrases without delay as an alternative of separately. This coaching is cheaper and seems to spice up accuracy as properly. “If you concentrate on the way you converse, if you’re midway via a sentence, what the remainder of the sentence goes to be,” says Zeiler. “These fashions must be able to that too.”
It has additionally discovered cheaper methods to create massive knowledge units. To coach final yr’s mannequin, DeepSeekMath, it took a free knowledge set known as Frequent Crawl—an enormous variety of paperwork scraped from the web—and used an automatic course of to extract simply the paperwork that included math issues. This was far cheaper than constructing a brand new knowledge set of math issues by hand. It was additionally simpler: Frequent Crawl contains much more math than every other specialist math knowledge set that’s out there.
And on the {hardware} aspect, DeepSeek has discovered new methods to juice outdated chips, permitting it to coach top-tier fashions with out coughing up for the newest {hardware} in the marketplace. Half their innovation comes from straight engineering, says Zeiler: “They undoubtedly have some actually, actually good GPU engineers on that staff.”
