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Can LLMs invent better ways to train LLMs?
Can LLMs invent better ways to train LLMs?
This not only reduces the need for extensive computational resources but also opens new avenues for exploring the vast search space of optimal loss functions, ultimately enhancing the capabilities of LLMs in various applications. In our latest report, Discovering Preference Optimization Algorithms with and for Large Language Models, we present a significant step towards automating the discovery of such application crucial approaches. Interestingly, throughout our experiments we observe that the LLM-driven discovery alternates between several different exploration, fine- tuning, and knowledge composition steps: E.g. for a classification task the LLM initially proposes a label-smoothed cross-entropy objective.
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