OpenMLE: An Open Full-Stack System for Recursive Self-Improvement
dair_ai · x · 2026-08-01
A fascinating paper on recursive self-improvement with the entire stack released.
- Core System: ML engineering provides a concrete, executable testbed for recursive self-improvement. OpenMLE is an open full-stack system spanning verifiable task environments with execution feedback, operator learning, and long-horizon search.
- Model Training: The team post-trained Frontis-MA1, a 35B meta-evolution agent aligned around four atomic program-evolution operators: Draft, Improve, Debug, and Crossover. These are trained via execution-grounded SFT and RL, then composed into long-horizon search.
- Performance: On MLE-Bench Lite under a 12-hour per-task budget (single RTX 4090 capped at 12GB VRAM), Medal Average climbed from 39.39% to 60.61%, reaching 71.21% with asynchronous search and benchmark-independent experience.
Related event: OpenMLE: An Open-Source Stack for Recursive AI Self-Improvement(3 posts)→
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