Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering
- Published
- Source
- arXiv
- Paper number
- 768
- Field
- LLMs / NLP
- arXiv ID
- 2607.28568
Key points
- It builds the full-stack OpenMLE system for AI-for-AI improvement and creates 5,758 executable task environments.
- It trains the meta-evolution agent Frontis-MA1-35B by using reinforcement learning to learn four evolutionary operators: drafting, improving, debugging, and crossover.
- On MLE-Bench Lite, the medal average rises from 39.4 percent to 71.2 percent, which beats GPT-5.5 plus Codex at 68.2 percent.
- A separate benchmark, NatureBench Lite, also confirms transfer learning, which shows that the result is not simple benchmark memorization.
- It releases the model weights, task environments, and training code, providing a reproducible basis for recursive self-improvement research.
Paper links
External research summaries. These are not HDATF publications or measured product results.