Tiny Recursion Model Achieves Strong AGI Benchmark Results with Minimal Parameters
By
guybedo
7mo ago· 2 min readenInsight
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Summary
The paper introduces Tiny Recursion Model (TRM), a recursive reasoning model that achieves impressive results on ARC-AGI benchmarks (45% on ARC-AGI-1 and 8% on ARC-AGI-2) using only 7 million parameters. The author argues against the prevailing belief that massive foundational models are necessary for solving hard tasks, advocating instead for recursive reasoning approaches that demonstrate 'less is more' efficiency.
Key quotes
· 4 pulledThe idea that one must rely on massive foundational models trained for millions of dollars by some big corporation in order to achieve success on hard tasks is a trap.
With recursive reasoning, it turns out that 'less is more': you don't always need massive models.
Currently, there is too much focus on exploiting LLMs rather than devising and expanding new lines of direction.
I propose Tiny Recursion Model (TRM), a recursive reasoning model that achieves amazing scores of 45% on ARC-AGI-1 and 8% on ARC-AGI-2 with a tiny 7M parameters neural network.
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