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
114 snips
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.
AI Snips
Chapters
Transcript
Episode notes
Niklas's Background
- Niklas Muennighoff, a PhD student at Stanford, was studying business and finance at Peking University.
- He switched to AI research, worked at Hugging Face, and is now a first-year PhD student.
Test-Time Scaling Approaches
- Test-time scaling has two approaches: parallel and sequential.
- Parallel scaling runs multiple computations independently and aggregates answers, while sequential scaling refines a single reasoning trace.
S1 vs. R1
- S1 and R1 both aim to replicate OpenAI's O1.
- R1 attempts a full replication of O1's pipeline, while S1 focuses on a minimal approach for reasoning and scaling.