Reproduce “Reasoning is Periodicity? Improving Large Language Models Through Effective Periodicity Modeling” on OpenResearch

About this paper

Yihong Dong, Ge Li, Xue Jiang, Yongding Tao, Kechi Zhang, Hao Zhu, Huanyu Liu, Jiazheng Ding and 3 more

FANformer improves learning efficiency and reasoning capabilities in large language models by integrating Fourier analysis into the attention mechanism. This architecture achieves comparable performance with 31% fewer parameters and 20% fewer training tokens than standard Transformer models while enhancing rule-based generalization.

Key insights

• Explicitly incorporating periodicity modeling through Fourier analysis within the attention mechanism fundamentally improves an LLM's capacity to learn recurring patterns and underlying rules.
• FANformer's architectural enhancements enable significant efficiency gains, requiring fewer parameters and less training data to achieve performance parity with standard Transformers.
• The periodicity-aware design fosters a shift from 'case-based' to 'rule-based' reasoning in LLMs, leading to superior generalization and more robust logical inference abilities.

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