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

This article from Practical AI discusses Niklas Muennighoff's research on the S1 model, a reasoning model inspired by OpenAI's O1. The focus is on S1's innovative approach to test-time scaling, including parallel and sequential methods, and its cost-effectiveness, with training costing under $50. The article highlights the model's data curation, training recipe, and use of distillation from Google Gemini and DeepSeek R1. It also explores the 'budget forcing' technique, evaluation benchmarks, and the comparison between supervised fine-tuning and reinforcement learning. The open-sourcing of S1 and its future directions are also discussed.

• •S1 is a reasoning model inspired by OpenAI's O1, trained at a low cost.
• •The article discusses S1's innovative test-time scaling techniques, including parallel and sequential methods.
• •The research explores the use of 'budget forcing' to optimize test-time compute and improve performance.