Less is More: Recursive Reasoning with Tiny Networks (Paper Review)

Author(s): Hira Ahmad

Originally published on Towards AI.

Less is More: Recursive Reasoning with Tiny Networks (Paper Review)

Modern AI often chases scale: deeper layers, more attention heads, and billions of parameters. But hidden beneath this race lies a quieter revolution: recursive reasoning, the idea that a model can improve its own thoughts, not by growing larger, but by thinking again.

The article examines two innovative AI architectures, Hierarchical Reasoning Models (HRM) and Tiny Recursion Models (TRM), highlighting how both approaches address the challenges of scale in AI reasoning. HRM attempts to simulate depth through its two-network structure, while TRM combines the strengths of recursion and a simplified design, enabling it to perform complex tasks with fewer parameters and greater efficiency. The author underscores the importance of adaptive computation and deep supervision in achieving robust reasoning without excess computational burden, positing a shift towards recursion as a means to enhance AI’s cognitive capabilities.

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Published via Towards AI