Sapients paper on the concept of Hierarchical Reasoning Model
9 months ago
- #Hierarchical Reasoning
- #Machine Learning
- #Artificial Intelligence
- Current large language models (LLMs) primarily use Chain-of-Thought (CoT) techniques, which have limitations like brittle task decomposition, high data requirements, and latency.
- The Hierarchical Reasoning Model (HRM) is proposed as a novel recurrent architecture inspired by hierarchical and multi-timescale processing in the human brain.
- HRM consists of two interdependent recurrent modules: a high-level module for slow, abstract planning and a low-level module for rapid, detailed computations.
- With only 27 million parameters and 1000 training samples, HRM achieves exceptional performance on complex reasoning tasks without pre-training or CoT data.
- HRM performs nearly perfectly on challenging tasks like complex Sudoku puzzles and optimal path finding in large mazes.
- HRM outperforms larger models with longer context windows on the Abstraction and Reasoning Corpus (ARC), a key benchmark for artificial general intelligence.
- HRM's results highlight its potential as a transformative advancement toward universal computation and general-purpose reasoning systems.