There’s extra. To make its use of reinforcement studying as environment friendly as potential, 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 unhealthy. Many current reinforcement-learning strategies require an entire separate mannequin to make this calculation. Within the case of huge language fashions, meaning a second mannequin that could possibly be as costly to construct and run as the primary. As a substitute 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 strategy
DeepSeek’s use of reinforcement studying is the primary innovation that the corporate describes in its R1 paper. However DeepSeek isn’t the one agency experimenting with this method. Two weeks earlier than R1 dropped, a workforce at Microsoft Asia introduced a mannequin known as rStar-Math, which was skilled in an identical approach. “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 strategies (however on high 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 elements in R1’s particular sauce.
What’s extra, it’s an open secret that high corporations like OpenAI, Google DeepMind, and Anthropic might already be utilizing their very own variations of DeepSeek’s strategy to coach their new technology of fashions. “I’m certain they’re doing nearly 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 one by one. This coaching is cheaper and seems to spice up accuracy as effectively. “If you consider the way you communicate, while you’re midway by way of a sentence, you understand 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 giant knowledge units. To coach final yr’s mannequin, DeepSeekMath, it took a free knowledge set known as Widespread 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: Widespread Crawl contains much more math than some other specialist math knowledge set that’s obtainable.
And on the {hardware} facet, DeepSeek has discovered new methods to juice previous chips, permitting it to coach top-tier fashions with out coughing up for the newest {hardware} available on the market. Half their innovation comes from straight engineering, says Zeiler: “They positively have some actually, actually good GPU engineers on that workforce.”
