Скачать или смотреть Tiny Recursive Model Outperforms Giant AI. TRM and HRM vs Deepseek R1, o3-mini, Gemini 2.5 Pro.

Tiny Recursive Model Outperforms Giant AI. TRM and HRM vs Deepseek R1, o3-mini, Gemini 2.5 Pro.

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Reference papers:
https://arxiv.org/pdf/2510.04871v1
https://arxiv.org/pdf/2506.21734

The podcast introduces two novel, biologically inspired AI architectures—the Hierarchical Reasoning Model (HRM) and the Tiny Recursive Model (TRM)—designed to enhance complex reasoning tasks while prioritizing efficiency over size. The initial paper proposes HRM, a 27M-parameter model that uses two coupled recurrent modules operating at different timescales to surpass larger Chain-of-Thought (CoT) models on challenging puzzles like Sudoku and mazes, achieving deep computational depth without extensive data or pre-training. The second and third sources then present TRM, a simplified and more efficient alternative with only 7M parameters, which dispenses with HRM's complex hierarchical structure and mathematical justifications, achieving even higher performance on these benchmarks. Both models demonstrate that recurrent, multi-step refinement processes can effectively address complex logical and algorithmic reasoning problems that remain difficult for massive Large Language Models (LLMs). TRM's superior performance with significantly fewer parameters challenges the prevailing AI trend of scaling models to ever-increasing sizes.