Evolutionary Multi-Task Optimization for LLM-Guided Program Discovery
EMO-STA is a two-stage framework for discovering reusable programs across related tasks using LLMs. It first evolves a shared archive of executable programs based on an aggregated family objective, then adapts these shared candidates to individual target tasks. This method outperforms single-task evolution by leveraging shared structure, leading to more generalizable and robust algorithms.