Frontis-MA1: Training an AI4AI Model Toward Recursive Self-Improvement in Machine Learning Engineering

FrontisAI introduces OpenMLE, an open full-stack environment for studying recursive self-improvement in machine learning engineering. It combines executable task feedback, operator learning, reinforcement learning, and long-horizon evolutionary search. Frontis-MA1, a 35B model, is post-trained around four program-evolution operators: Draft, Improve, Debug, and Crossover. According to the provided abstract, the system raises Medal Average on MLE-Bench Lite from 39.39% to 60.61%, or 71.21% with OpenMLE-Evo-Max, under a 12-hour budget on one RTX 4090 capped at 12 GB VRAM. Transfer results are also reported on NatureBench Lite.