Can LLMs invent better ways to train LLMs?

Sakana AI explores using Large Language Models (LLMs) for inventing better ways to train themselves, termed LLM². They leverage evolutionary algorithms to develop novel preference optimization techniques, significantly improving model performance. Their latest report introduces 'Discovered Preference Optimization (DiscoPOP)', achieving state-of-the-art results across various tasks with minimal human intervention. The approach promises a new paradigm of AI self-improvement, reducing extensive trial-and-error efforts traditionally required in AI research.

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