# S1 paper explains inference-time scaling by extending an LLM's reasoning

DevFeed: [S1 paper explains inference-time scaling by extending an LLM's reasoning](<https://devfeed.tech/articles/s1-the-6-r1-competitor-33454.md>)

Original publisher: [Read original article](<https://timkellogg.me/blog/2025/02/03/s1>)

Published: 2025-02-03T00:00:00Z

Content type: opinion

Language: en

Sources: [Tim Kellogg](<https://devfeed.tech/sources/tim-kellogg.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [scaling laws](<https://devfeed.tech/topics/scaling-laws.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [openai](<https://devfeed.tech/tags/openai.md>), [paper](<https://devfeed.tech/tags/paper.md>), [scaling-laws](<https://devfeed.tech/tags/scaling-laws.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

This commentary discusses the S1 paper, which describes a model that is slightly below state of the art but can run on the author's laptop. It focuses on inference-time scaling and the paper's explanation of extending an LLM's reasoning by forcing it to continue after it tries to stop, along with connections to token selection and Entropix.

## Source excerpt

A new paper released on Friday is making waves in the AI community, not because of the model it describes, but because it shows how close we are to some very large breakthroughs in AI. The model is just below state of the art, but it can run on my laptop. More important, it sheds light on how all this stuff works, and it's not complicated.