LLM self-evolution: Shinka Evolve, AlphaEvolve, and sample efficiency

大语言模型的自我进化 | 罗伯特·兰格 | Shinka Evolve | AlphaEvolve | 样本效率大幅提升 | 《伟大无法被规划》| POET | PowerPlay | UCB老虎机

Sakana AI open-sourced Shinka Evolve and uses a UCB bandit to switch among GPT-5, Claude Sonnet 4.5, Gemini, and others, aiming to cut the thousands of program evaluations common in AlphaEvolve-style search. The post says it beat AlphaEvolve’s classic circle-packing result with fewer evaluations and adds full-file rewrites, crossover, editable-region guards, and a meta-notebook; the post does not disclose exact metrics, cost, or the repo link. The part to watch is surrogate-task design and hard verification: the system still needs humans to define problems.

Why it matters: Featured, not P1: HKR-H/K/R all pass. The piece has a strong hook, concrete mechanisms like UCB model routing and program crossover, and a real nerve around eval cost and hard verification. It stays at 80 because key metrics, cost, and the primary release link are not disclosed.