February 21, 2024Open Access

On the Expressive Power of a Variant of the Looped Transformer

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The proposed AlgoFormer achieves significantly higher expressive power in algorithm representation than standard and vanilla looped transformer models under equal parameter budgets.
Theoretical analysis and empirical evaluations confirm superior performance across challenging tasks using a pre-transformer, an iterative looped transformer, and a post-transformer.
These findings highlight that structured iterative optimization algorithms embedded in transformer blocks have the potential to outperform conventional human-designed algorithms.

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Implication

Theoretical evaluation demonstrates superior algorithmic expressive power in the AlgoFormer architecture, highlighting potential to exceed human-designed optimization routines.

Cite This Study

Gao et al. (2024) studied this question.

synapsesocial.com/papers/68e785a2b6db6435876f7d68 https://doi.org/10.48550/arxiv.2402.13572

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Implication

Theoretical evaluation demonstrates superior algorithmic expressive power in the AlgoFormer architecture, highlighting potential to exceed human-designed optimization routines.

On the Expressive Power of a Variant of the Looped Transformer

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Discussion

Implication

Cite This Study

On the Expressive Power of a Variant of the Looped Transformer

Key Points

Discussion

Implication

Cite This Study

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