How can AI models improve their reasoning at test time without extensive retraining? In this episode, we explore the groundbreaking paper “Simple Test-Time Scaling” by researchers from Stanford University, University of Washington, and the Allen Institute for AI. This research introduces a novel approach to scaling reasoning performance by using extra compute at inference time, surpassing even OpenAI’s latest models.   Join us as we break down the key findings, discuss the implications for AI in education, and explore how techniques like budget forcing can enhance learning and decision-making. 🚀   📢 This episode is part of our ongoing season, where SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Join us as we explore the future of education in an AI-integrated world.   🔗 Read the full paper: https://github.com/simplescaling/s1   We are: ✅ Microsoft Global Training Partner, MCTs & AI Thought Leaders from Ecuador 🇪🇨 ✅ Democratizing AI for educators, students, and institutions ✅ Merging EdTech & AI for next-generation learning experiences   🎯 What We Offer: 🔹 Comprehensive frameworks and digital transformation programs for schools and universities through our partnership with Microsoft 🔹 Cutting-edge research explained clearly for educators and leaders 🔹 Innovative learning strategies with AI and technology   💡 Explore more free resources: 🔸 Research articles and essays on Substack 🔸 Podcasts created with Google LM in this new season 🎙 🔸 AI-powered TikTok posts that encourage reading 🔸 Music for cognitive learning and focus 🎼   📢 Follow @ShifterLabsEC for exclusive AI & EdTech content, and don’t miss the latest edition of our successful bootcamp, “The Rise of Generative AI in Education.   ShifterLabs is Ecuador’s premier EdTech innovator and Microsoft Global Training Partner. Visit us at shifterlabs.com.