Tiny Recursion Model Achieves Strong AGI Benchmark Results with Only 7M Parameters
By
stared
Plain bagel done well. Pleasantly substantive.
Summary
The paper introduces Tiny Recursion Model (TRM), a recursive reasoning model that achieves impressive scores of 45% on ARC-AGI-1 and 8% on ARC-AGI-2 using only 7M parameters. The author argues against the prevailing belief that massive foundational models are necessary for success on hard tasks, advocating instead for recursive reasoning approaches where 'less is more.' The work challenges the current focus on exploiting large language models and promotes developing new research directions.
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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