Less is More: Recursive Reasoning with Tiny Networks

Tiny Brains, Big Wins: Solving Hard Puzzles with Less

Researchers found that thinking small can beat thinking big on some tough puzzles.
A method that used two tiny nets looping at different speeds did well, but a simpler idea does even better.
Meet the Tiny Recursive Model, it uses just one small network and it loops its own steps to reason.
With only 7M parameters this little model learns patterns that larger systems often miss.
It gets about 45% on ARC-AGI-1 and some success on harder sets too, while using way less compute than big models.
That means small teams or hobbyists can try things that used to need huge machines.
This approach shows how small networks can tackle hard puzzles like visual tasks and logic problems, sometimes better than much larger systems.
The trick seems to be repeating simple steps again and again, not stacking tons of layers.
It’s promising, not perfect yet, but it points to smarter, cheaper ways to build problem solvers people can run on normal hardware.

Read article comprehensive review in Paperium.net:
Less is More: Recursive Reasoning with Tiny Networks

🤖 This analysis and review was primarily generated and structured by an AI . The content is provided for informational and quick-review purposes.