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I propose a novel approach to training large language models (LLMs), inspired by the layered learning process observed in humans. Instead of training on all data simultaneously, this method would introduce increasingly complex information in stages, prioritizing foundational knowledge and relevance to the modern world. This "back-to-front" training approach could potentially improve the efficiency and effectiveness of LLM training. I've outlined the concept in more detail in this Gist:
https://gist.github.com/SMeyersMrOvkill/14ff37ffb955831897b177fb3d2d540e.
While the core idea and solutions presented in the Gist are my own, I'd like to acknowledge the valuable assistance I received from a language model in refining the presentation of this concept, making it clearer and more engaging for the community. I'm eager to hear your thoughts and feedback!
https://gist.github.com/SMeyersMrOvkill/14ff37ffb955831897b177fb3d2d540e.
While the core idea and solutions presented in the Gist are my own, I'd like to acknowledge the valuable assistance I received from a language model in refining the presentation of this concept, making it clearer and more engaging for the community. I'm eager to hear your thoughts and feedback!