This episode explores the Tiny Recursive Model (TRM), a novel approach that leverages a single, tiny network (as small as 7M parameters) to tackle hard puzzle tasks like Sudoku, Maze, and ARC-AGI. We investigate how this simplified, recursive reasoning strategy achieves significantly higher generalization and outperforms much larger models, including complex Large Language Models (LLMs) and the Hierarchical Reasoning Model (HRM). Discover why this "less is more" philosophy is leading to breakthroughs in parameter-efficient AI reasoning by simplifying complex mathematical theories and biological justifications.