Can Large Language Models Invent Algorithms to Improve Themselves?

• URL: http://arxiv.org/abs/2410.15639v2
• Date: Tue, 22 Oct 2024 03:14:46 GMT
• Title: Can Large Language Models Invent Algorithms to Improve Themselves?
• Authors: Yoichi Ishibashi, Taro Yano, Masafumi Oyamada,
• Abstract summary: Large Language Models (LLMs) have shown remarkable performance improvements and are rapidly gaining adoption in industry. We propose the Self-Developing framework, which enables LLMs to autonomously generate and learn model-improvement algorithms. In mathematical reasoning tasks, Self-Developing not only creates models that surpass the seed model but also consistently outperforms models created using human-designed algorithms.
• Score: 3.6117068575553595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown remarkable performance improvements and are rapidly gaining adoption in industry. However, the methods for improving LLMs are still designed by humans, which restricts the invention of new model-improving algorithms to human expertise and imagination. To address this, we propose the Self-Developing framework, which enables LLMs to autonomously generate and learn model-improvement algorithms. In this framework, the seed model generates, applies, and learns model-improving algorithms, continuously improving both the seed model and the algorithms themselves. In mathematical reasoning tasks, Self-Developing not only creates models that surpass the seed model but also consistently outperforms models created using human-designed algorithms. Additionally, these LLM-discovered algorithms demonstrate strong effectiveness, including transferability to out-of-domain models.

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