Jiarui Yuan

[Jiarui Yuan @Yuan3141]

Recursive self-improvement (RSI) is a long-term vision. OpenMLE takes a concrete step toward it in machine learning engineering (MLE), where agents iteratively improve ML solutions through execution-grounded feedback and experience-driven search.

[Junlin Yang @junlin45300]

🚀 OpenRSI is a new open research series from @FrontisAI for concrete, testable progress toward recursive self-improvement (RSI).

As its first project—and also my first work as first author—I’m proud to present OpenMLE: an open full-stack AI4AI system for autoresearch, where evolutionary agents improve ML solutions through executable feedback.

OpenMLE has three components:
- OpenMLE-Gym: 5,758 executable tasks + evaluators
- OpenMLE-ERL: execution-grounded SFT + RL
- OpenMLE-Evo: experience-guided long-horizon search

🏆 The full system—our trained Frontis-MA1-35B model paired with OpenMLE-Evo-Max—reaches 71.21% Medal Average on MLE-Bench Lite: surpassing GPT-5.5 + Codex (68.18%) and just 1.52% from GPT-5.6 Sol + Codex and the 2.8T Kimi K3 + Claude Code (72.73%). Budget: 12 hours/task on one RTX 4090 capped at 12 GB VRAM.

🌍 On 10 held-out NatureBench Lite tasks, both components transfer:

• same framework, model swap: Match-SOTA 50% → 70%
• same base model, framework swap: Match-SOTA 20% → 50%

🔓 Paper, code, models, data, and analysis below. 🧵