metaevolve-8da96334·1 events·first seen Aliases: MetaEvolve
Researchers introduce MetaEvolve, a framework that cultivates 'meta-skills' such as self-reflection and multi-round refinement in LLMs through a data synthesis pipeline and evolution-aware reinforcement learning. Grounded in coding tasks where program execution provides continuous reward signals, the system trains on evolution trajectories containing programs, fitness scores, and attempt histories. MetaEvolve outperforms the strongest baseline by 10.01% on in-distribution coding benchmarks and 24.12% on out-of-distribution tasks, with a 46.9% relative improvement on open-ended algorithm optimization problems outside the training domain. The work is motivated by AlphaEvolve-style test-time scaling and aims to produce generalizable meta-skills transferable to domains with sparse training signals.