Recent progress in large-scale language models has enabled breakthroughs in previously intractable computer programming tasks. Prior work in meta-learning and neural architecture search has led to substantial successes across various task domains, spawning myriad approaches for algorithmically optimizing the design and learning dynamics of deep learning models. At the intersection of these research areas, we implement a code-generating language model with the ability to modify its own source code. Self-programming AI algorithms have been of interest since the dawn of AI itself. Although various theoretical formulations of generalized self-programming AI have been posed, no such system has been successfully implemented to date under real-world computational constraints. Applying AI-based code generation to AI itself, we develop and experimentally validate the first practical implementation of a self-programming AI system. We empirically show that a self-programming AI implemented using a code generation model can successfully modify its own source code to improve performance and program sub-models to perform auxiliary tasks. Our model can self-modify various properties including model architecture, computational capacity, and learning dynamics.

翻译：近年大语言模型的进展使此前难以解决的计算编程任务取得突破。元学习与神经架构搜索领域的前期研究已在多种任务领域中取得显著成功，催生了大量算法方法，用于优化深度学习模型的设计与学习动态。在这些研究领域的交叉点上，我们实现了一种能够修改自身源代码的代码生成语言模型。自人工智能诞生之初，自我编程AI算法便备受关注。尽管已有多种广义自我编程AI的理论框架被提出，但在现实计算约束下，迄今尚未有系统被成功实现。我们首次将基于AI的代码生成技术应用于AI本身，开发并通过实验验证了自我编程AI系统的首个实际实现。实验表明，基于代码生成模型实现的自我编程AI能够成功修改自身源代码以提升性能，并编程子模型执行辅助任务。我们的模型可实现多种属性的自我修改，包括模型架构、计算能力与学习动态。