# The $6 Reasoning Model: Breaking Down Stanford's S1 Paper

Page: https://stenobird.com/podcast/generative-ai-meetup/the-6-reasoning-model-breaking-down-stanford-s-s1-paper
Text version: https://stenobird.com/podcast/generative-ai-meetup/the-6-reasoning-model-breaking-down-stanford-s-s1-paper.md
Podcast: [The Generative AI Meetup Podcast](https://stenobird.com/podcast/generative-ai-meetup)
Published: 2025-02-19T04:43:39+00:00
Episode link: https://podcast.genaimeetup.com/e/the-6-reasoning-model-breaking-down-stanfords-s1-paper/
Audio file: https://mcdn.podbean.com/mf/web/wi6huwsiuwnyn4xi/i-know-it-when-i-see-it-podcast-audio.mp3
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JSON: https://stenobird.com/v1/public/podcasts/generative-ai-meetup/episodes/the-6-reasoning-model-breaking-down-stanford-s-s1-paper
Duration seconds: 4622

## Resource

Podcast: https://podcast.genaimeetup.com/ Youtube: https://www.youtube.com/@GenerativeAIMeetup In this episode, we explore Stanford's groundbreaking S1 paper, which introduces a technique to transform any language model into a reasoning model for just $6 in computation costs. We dive deep into the implications of this research, discussing budget forcing techniques, the true costs of AI development, and the philosophical limits of artificial intelligence across different domains - from mathematical reasoning to language translation. The conversation extends to broader questions about superhuman AI capabilities and the fundamental limitations in various fields like translation, history, and agriculture. Join us for an insightful discussion on the future of AI reasoning and its practical applications. 0:00 - Intro and weekly AI news overview1:06 - Introduction to the S1 paper from Stanford1:40 - Explanation of reasoning models vs single-shot models2:22 - Details of Stanford's S1 technique and QEN32B model3:00 - Cost comparison with other models ($6 training cost)4:04 - Discussion of model distillation technique5:00 - Budget forcing explanation6:33 - Story about building an AI stock research agent8:22 - Philosophical discussion on reasoning limits

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