Less is more: Recursive reasoning with tiny networks
SMRTR summary
A new 7-million parameter neural network called Tiny Recursion Model achieves impressive results on challenging reasoning tasks by repeatedly improving its answers through recursive self-correction, scoring 45% on ARC-AGI-1 tests. This approach proves that expensive, massive AI models aren't always necessary for complex problem-solving, as smaller networks can achieve strong performance through iterative reasoning processes.
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