ShinkaEvolve: Evolving New Algorithms with LLMs, Orders of Magnitude More Efficiently

Sakana AI

Sakana AI released ShinkaEvolve, a framework that uses LLMs to evolve and discover new algorithms with dramatically improved sample efficiency compared to prior evolutionary approaches. The system discovered a state-of-the-art Circle Packing solution using only 150 samples, designed an effective math competition agent scaffold in 75 generations, and found a novel loss function for Mixture-of-Experts models after 30 generations. The open-source framework enables researchers and engineers to use evolutionary AI discovery as a practical tool for optimizing algorithms and training strategies across multiple domains.