The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Inside s1: An o1-Style Reasoning Model That Cost Under $50 to Train with Niklas Muennighoff - #721
Mar 3, 2025
Niklas Muennighoff, a PhD student at Stanford, dives into his groundbreaking work on the S1 reasoning model, designed to efficiently mimic OpenAI's O1 while costing under $50 to train. He elaborates on innovative techniques like 'budget forcing' that help the model tackle complex problems more effectively. The discussion highlights the intricacies of test-time scaling, the importance of data curation, and the differences between supervised fine-tuning and reinforcement learning. Niklas also shares insights on the future of open-sourced AI models.
49:29
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Quick takeaways
- The S1 model introduces a budget forcing technique that optimizes computational effort during reasoning by regulating answer generation based on token budgets.
- S1's open-source nature and minimal resource requirements foster accessibility and promote further experimentation in AI reasoning applications among researchers.
Deep dives
Comparison of S1 and R1 Approaches
The S1 and R1 models seek to replicate the functionality of OpenAI's O1 model, but they do so with different methodologies. R1 aims to replicate the entire pipeline established by O1, striving for a comprehensive reconstruction of its functionalities. In contrast, S1 is focused on achieving the core benefits of O1—strong reasoning performance and test time scaling—through a more minimalistic approach. This strategic difference has implications for the complexity and resource demands of each model, with S1 designed to be more accessible and cost-effective.
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