Less is More: Recursive Reasoning with Tiny Networks

This October 6, 2025 paper from Alexia Jolicoeur-Martineau at Samsung SAIL Montréal, provides an overview and detailed comparison of two recurrent reasoning models: the **Hierarchical Reasoning Model (HRM)** and the proposed **Tiny Recursive Model (TRM)**. HRM is a complex, biologically-inspired approach that uses two small neural networks and **deep supervision** to outperform Large Language Models (LLMs) on difficult puzzle tasks like Sudoku and ARC-AGI. The authors introduce TRM as a simpler, more efficient alternative that utilizes a single tiny, two-layer network to achieve **superior generalization** while using significantly fewer parameters than both HRM and powerful LLMs. The document highlights that TRM simplifies HRM by removing the need for fixed-point theorems and complex biological justifications, instead relying on **recursive reasoning** to progressively refine its predicted answer, leading to state-of-the-art results on several benchmarks.Sources:https://arxiv.org/html/2510.04871v1https://www.artificialintelligence-news.com/news/samsung-tiny-ai-model-beats-giant-reasoning-llms/

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