You can train smaller models to autonomously improve ML code and configurations by teaching them to apply specific program-evolution operators in a loop, enabling practical AI-assisted ML engineering without massive compute.
This paper introduces Frontis-MA1, a 35B AI model trained to improve machine learning engineering tasks through recursive self-improvement. The system uses four core operators (Draft, Improve, Debug, Crossover) to iteratively refine ML solutions, achieving 71% performance on benchmarks—comparable to much larger models—while running on a single GPU with 12GB memory.