Sebastian Raschka, PhD (@rasbt): "From the Hierarchical Reasoning Model (HRM) to a new Tiny Recursive Model (TRM). A few months ago, the HRM made big waves in the AI research community as it showed really good performance on the ARC challenge despite its small 27M size. (That's about 22x smaller than the smalle…"

A few months ago, the HRM made big waves in the AI research community as it showed really good performance on the ARC challenge despite its small 27M size. (That's about 22x smaller than the smallest Qwen3 0.6B model.) Now, the new "Less is More: Recursive Reasoning with Tiny Networks" paper proposes Tiny Recursive Model (TRM), which a simpler and even smaller model (7M, 4× smaller than HRM) that performs even better on the ARC challenge. 🔹 What does recursion mean here? TRM refines its answer in two steps: 1. It updates a latent (reasoning) state from the current question and answer. 2. Then it updates the answer based on that latent state. Training runs for up to 16 refinement steps per batch. Each step does several no-grad loops to improve the answer, followed by one gradient loop that learns from the full reasoning process. By the way, the question and the answer are grids of discrete tokens, not text. (E.g., 9×9 Sudoku and up to 30×30 ARC and Maze.) 🔹 And how does it differ from

A few months ago, the HRM made big waves in the AI research community as it showed really good performance on the ARC challenge despite its small 27M size. (That's about 22x smaller than the smallest Qwen3 0.6B model.) Now, the new "Less is More: Recursive Reasoning with Tiny Networks" paper proposes Tiny Recursive Model (TRM), which a simpler and even smaller model (7M, 4× smaller than HRM) that performs even better on the ARC challenge. 🔹 What does recursion mean here? TRM refines its answer in two steps: 1. It updates a latent (reasoning) state from the current question and answer. 2. Then