Researchers at Stanford and the University of Washington have trained a new reasoning model called S1 using less than $50 in cloud compute credits, demonstrating the increasing accessibility of AI language models. S1, a competitor to OpenAI's o1, is based on an off-the-shelf language model fine-tuned using 1,000 curated questions and answers from Google's Gemini 2.0 Flash. The researchers also discovered that adding the word 'wait' during S1's reasoning process helped improve accuracy, highlighting the potential for further refinement through simple techniques.
It is becoming increasingly clear that AI language models are a commodity tool, as the sudden rise of open source offerings likeshow they can be hacked together without billions of dollars in venture capital funding. A new entrant called S1 is once again reinforcing this idea, as researchers at Stanford and the University of Washington trained the “reasoning” model using less than $50 in cloud compute credits.
Another interesting detail is how the researchers were able to improve the reasoning performance of S1 using an ingeniously simple method: This suggests that, despite worries that AI models are hitting a wall in capabilities, there remains a lot of low-hanging fruit. Some notable improvements to a branch of computer science are coming down to conjuring up the right incantation words. It also shows how crude chatbots and language models really are; they do not think like a human and need their hand held through everything.
There has been a lot of debate about what the rise of cheap, open source models might mean for the technology industry writ large. Is OpenAI doomed if its models can easily be copied by anyone? Defenders of the company say that language models were always destined to be commodified. OpenAI, along with Google and others, will succeed building useful applications on top of the models.
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