TurboEvolve: Towards Fast and Robust LLM-Driven Program Evolution
LLM-driven program evolution can discover high-quality programs, but high API cost and run-to-run variance hinder reliable progress. We propose TurboEvolve, a multi-island evolutionary framework that improves sample efficiency and stability under matched API-cost budgets. Inspired by multiple-offspring strategies in evolutionary algorithms, TurboEvolve introduces Verbalized Sampling, which elicits K diverse candidates in one LLM call while using self-assigned probabilities only as generation signals, together with an online scheduler that adapts K to expand exploration under stagnation and reduce redundant generation during steady progress. To exploit existing solution pools, TurboEvolve ...
| Authors | Yang Yang, Zining Zhong, Jindong Li et al. |
|---|---|
| Primary category | cs.NE |
| Published | 12 April 2026 |
| Last updated at source | 12 October 2026 |
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