A tiny recursive reasoning model achieves 45% on ARC-AGI-1 and 8% on ARC-AGI-2
SMRTR summary
A new 7-million parameter neural network called Tiny Recursion Model proves that small AI systems can tackle complex reasoning tasks by recursively improving their answers over time, achieving 45% accuracy on the challenging ARC-AGI-1 benchmark. This approach challenges the assumption that only massive, expensive AI models can solve difficult problems, demonstrating that strategic recursive reasoning can outperform brute-force scaling methods.
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