Chain-of-Thought prompting offers more memory than looped Transformers

Researchers have explored the differences in memory usage between Chain-of-Thought prompting and looped Transformers. They found that Chain-of-Thought utilizes generated tokens as a persistent scratchpad, while looped Transformers rely on recurrent hidden activations. The study indicates that compressed loops are constrained by their recurrent state size, limiting their ability to solve complex problems compared to Chain-of-Thought, which can handle P-complete tasks. AI

IMPACT This research clarifies memory-budget differences between two transformer reasoning methods, potentially guiding future model design for complex tasks.

RANK_REASON This is a research paper detailing a new method for improving transformer models. [lever_c_demoted from research: ic=1 ai=1.0]

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• Looped Transformers

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