A few implications of tricks like this:
1) We are still VERY early in the development of Reasoners
2) There is high value in understanding how humans solve problems & applying that to AI
3) Higher possibility of further exponential growth in AI capabilities as techniques for thinking traces compound
1) We are still VERY early in the development of Reasoners
2) There is high value in understanding how humans solve problems & applying that to AI
3) Higher possibility of further exponential growth in AI capabilities as techniques for thinking traces compound
Reposted from
Ethan Mollick
This paper is wild - a Stanford team shows the simplest way to make an open LLM into a reasoning model
They used just 1,000 carefully curated reasoning examples & a trick where if the model tries to stop thinking, they append "Wait" to force it to continue. Near o1 at math. arxiv.org/pdf/2501.19393
They used just 1,000 carefully curated reasoning examples & a trick where if the model tries to stop thinking, they append "Wait" to force it to continue. Near o1 at math. arxiv.org/pdf/2501.19393
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