Inside s1: An o1-Style Reasoning Model That Cost Under $50 to Train with Niklas Muennighoff - #721

2025-03-03 · 49 min · episode 721 · 13 entities

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→ appeared on The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence podcast

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Inside s1: An o1-Style Reasoning Model That Cost Under $50 to Train with Niklas Muennighoff - #721
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→ discusses Science concept

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→ discusses Technology company

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→ discusses Tech News concept

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→ hosted by TWIML company

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Feed author/publisher: TWIML

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discusses Science concept

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discusses News concept

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discusses Tech News concept

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hosted by TWIML company

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mentioned Niklas Muennighoff person

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Inside s1: An o1-Style Reasoning Model That Cost Under $50 to Train with Niklas Muennighoff - #721
mentioned Sam Charrington person

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Feed author/publisher: Sam Charrington
hosted by Sam Charrington person

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evidence rules-v4
Feed author/publisher: Sam Charrington

podcasts 1

appeared on The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence podcast

0.68

evidence rules-v4
Inside s1: An o1-Style Reasoning Model That Cost Under $50 to Train with Niklas Muennighoff - #721

Episode description as stored

Today, we're joined by Niklas Muennighoff, a PhD student at Stanford University, to discuss his paper, “S1: Simple Test-Time Scaling.” We explore the motivations behind S1, as well as how it compares to OpenAI's O1 and DeepSeek's R1 models. We dig into the different approaches to test-time scaling, including parallel and sequential scaling, as well as S1’s data curation process, its training recipe, and its use of model distillation from Google Gemini and DeepSeek R1. We explore the novel "budget forcing" technique developed in the paper, allowing it to think longer for harder problems and optimize test-time compute for better performance. Additionally, we cover the evaluation benchmarks used, the comparison between supervised fine-tuning and reinforcement learning, and similar projects like the Hugging Face Open R1 project. Finally, we discuss the open-sourcing of S1 and its future directions. The complete show notes for this episode can be found at https://twimlai.com/go/721.