Large language models (LLMs) are rapidly transforming industries, but their improvement still heavily relies on human ingenuity. What if AI could take the reins and develop its own algorithms for self-improvement? Researchers are exploring this exciting possibility with a novel framework called "Self-Developing." This framework allows LLMs to generate and apply model-improvement algorithms autonomously, potentially unlocking performance gains beyond human capabilities. The process involves an LLM "algorithm factory" that generates Python code representing various improvement strategies. These algorithms are then applied to a "seed model," and the resulting improved models are evaluated on mathematical reasoning tasks like GSM8k and MATH. Surprisingly, the LLM-discovered algorithms not only outperformed the original seed model but also surpassed models improved with established human-designed algorithms by a significant margin—up to 4.3% on GSM8k. This success stems from the framework's ability to iteratively refine both the seed model and the algorithm factory, leading to increasingly effective algorithms. Even more intriguing, these LLM-generated algorithms showed strong transferability. They effectively improved out-of-domain models, meaning models not used in the initial algorithm generation process, exceeding the performance of human-designed algorithms on these new models by a remarkable 7.4%. This adaptability opens doors to more robust and versatile AI systems. While still in its early stages, this research into self-improving LLMs offers a glimpse into a future where AI can bootstrap its own development, potentially unlocking unforeseen advancements and accelerating the pace of AI evolution. However, challenges remain in managing the complexity and potential unintended consequences of such self-modification. The exploration of efficient and safe self-improvement mechanisms will be crucial as we venture further into this frontier of artificial intelligence.