Executive Summary
The research paper Less is More: Recursive Reasoning with Tiny Networks introduces a breakthrough in artificial intelligence efficiency, showing that small, intelligently designed models can outperform massive large language models on complex reasoning tasks. Developed by researchers at Samsung SAIL Montréal, the Tiny Recursive Model uses a lightweight recursive structure that mimics human problem-solving, iteratively refining its own answers, rather than relying on brute computational scale. For business leaders, this marks a turning point in AI economics: it demonstrates that “smarter” design can replace “larger” infrastructure, reducing energy costs, improving speed, and enabling powerful reasoning capabilities to run on smaller, more sustainable hardware. The research challenges the trillion-parameter mindset dominating today’s AI landscape, signaling a shift toward compact, transparent, and cost-efficient models that could democratize advanced AI across industries.
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Key point: This paper demonstrates that tiny recursive neural networks can outperform massive large language models in reasoning tasks, proving that intelligent model design can achieve greater efficiency and capability with far fewer computational resources.
Less is More: Recursive Reasoning with Tiny Networks
Overview of the Paper
The research paper Less is More: Recursive Reasoning with Tiny Networks (Alexia Jolicoeur-Martineau, Samsung SAIL Montréal, October 2025) introduces the Tiny Recursive Model (TRM), a new approach to reasoning that dramatically simplifies and improves upon the Hierarchical Reasoning Model (HRM). Instead of relying on massive Large Language Models (LLMs), TRM achieves comparable or even superior reasoning performance using a tiny neural network with only 7 million parameters. Tested on demanding benchmarks such as Sudoku-Extreme, Maze-Hard, and ARC-AGI, TRM demonstrates that recursive reasoning with small models can outperform LLMs that are over 100,000 times larger.
Key Contributions
Tiny Recursive Architecture. TRM replaces HRM’s two-network, biologically-inspired hierarchy with a single lightweight network that recursively refines its answers.
No Fixed-Point or Complex Theorems. It eliminates reliance on the Implicit Function Theorem and hierarchical assumptions, simplifying both training and theory.
Efficiency and Performance. With only 7M parameters, TRM outperforms HRM (27M parameters) on several reasoning benchmarks, including a 32% accuracy boost on Sudoku-Extreme (55% → 87%) and better results on ARC-AGI-1 and ARC-AGI-2.
Generalization Through Simplicity. The model’s “less is more” design achieves higher generalization and lower overfitting, proving that depth through recursion can outperform width through scaling.
Significance of the Findings
This research challenges the prevailing assumption that intelligence in AI requires ever-larger models. TRM shows that recursion and refinement, not sheer size, drive effective reasoning. By demonstrating human-level reasoning capabilities in small networks trained on minimal data, it opens new avenues for efficient AI development, especially in low-resource or embedded environments. The model also provides a theoretical and practical framework for recursive reasoning without expensive backpropagation through time, making deep reasoning more computationally affordable.
Why It Matters
For business and technology leaders, the implications are profound: TRM represents a shift from “bigger is better” to “smarter is smaller.” This efficiency could lower AI infrastructure costs, enable on-device reasoning, and make AI explainability and governance more tractable. As organizations face growing compute and sustainability challenges, models like TRM suggest that the next leap in AI capability may come not from trillion-parameter models, but from cleverly designed, small recursive systems that think more like humans - iteratively, economically, and transparently.
Reference
Vasudevan, V., Mahankali, A., Hossain, M., & Arefeen, M. (2025). Less is more: Recursive reasoning with tiny networks. Samsung SAIL Montreal. arXiv preprint arXiv:2510.04871. https://arxiv.org/abs/2510.04871
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Samsung SAIL Montreal
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