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Author: Admin | 2025-04-28
The process exemplifies what many researchers say is a paradigm shift: instead of scaling the amount of computing power used to train the model, researchers scale the amount of time (and thus, computing power and electricity) the model uses to think about a response to a query before answering. It is this scaling of what researchers call “test-time compute” that distinguishes the new class of “reasoning models,” such as DeepSeek R1 and OpenAI’s o1, from their less sophisticated predecessors. Many AI researchers believe there’s plenty of headroom left before this paradigm hits its limit.Some AI researchers hailed DeepSeek’s R1 as a breakthrough on the same level as DeepMind’s AlphaZero, a 2017 model that became superhuman at the board games Chess and Go by purely playing against itself and improving, rather than observing any human games.That’s because R1 wasn’t “pretrained” on human-labeled data in the same way as other leading LLMs. Instead, DeepSeek’s researchers found a way to allow the model to bootstrap its own reasoning capabilities essentially from scratch.“Rather than explicitly teaching the model on how to solve a problem, we simply provide it with the right incentives, and it autonomously develops advanced problem-solving strategies,” they claim. The finding is significant because it suggests that powerful AI capabilities might emerge more rapidly and with less human effort than previously thought, with just the application of more computing power. “DeepSeek R1 is like GPT-1 of this scaling paradigm,” says Ball.Ultimately, China’s recent AI progress, instead of usurping U.S. strength, might in
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