LEVI: cheaper small models beat expensive LLMs at algorithm discovery

Systems optimization should be part of CI/CD

UCB's ADRS team released LEVI, a framework that cuts algorithm discovery cost to 1/3–1/7 of baselines. Instead of using the most expensive models for every step, smaller models like QWEN 30B handle most mutations, while frontier models are reserved for rare paradigm shifts. LEVI maintains diversity across both code structure and runtime behavior to prevent the search from collapsing. The team argues ADRS should become a CI/CD step that re-optimizes algorithms nightly against actual traffic, hardware, and SLOs. The post does not disclose specific benchmark scores or baseline names.

Why it matters: LEVI cuts algorithmic discovery cost to 1/3 with a clear strategy: cheap models for mutations, expensive models only for paradigm shifts. Directly useful for people doing auto-optimization and CI/CD. Not p1 because it's an engineering technique rather than an industry-shaking ...