The Self-Taught Optimizer (STOP) framework explores recursive self-improvement in code generation using language models. It introduces a seed "improver" program that refines itself iteratively, leading to improved performance across various algorithmic tasks. The study delves into self-improvement strategies proposed by the language model, transferability to new tasks, and concerns regarding safety measures like sandbox bypassing. Additionally, it highlights the importance of understanding and mitigating negative impacts of advanced language models.
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by Eric Zelikma... alle arxiv.org 03-04-2024
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