MLExpert: Open 35B MoE Agentic Model Lifts MLE-Bench Lite Medal Rate from 37.9% to 72.7%, Matching Kimi-K3 and GLM-5.3
Advancing autonomous AI-for-AI (AI4AI) engineering, researchers from Shanghai AI Lab (InternScience) release MLExpert (arXiv:2610.22344), a 35B Mixture-of-Experts agentic model for autonomous machine learning engineering. Powered by the Kaggle-MLE Factory, hierarchical agentic SFT, and Windowed Interaction RL (WIRL), MLExpert boosts average medal rates on MLE-Bench Lite from 37.9% to 72.7%—matching frontier giants like Kimi-K3 and GLM-5.3 while generalizing to agent scaffold optimization.
Key Takeaways
- 72.7% medal rate on MLE-Bench Lite: boosts base model performance from 37.9% to 72.7%, matching frontier models Kimi-K3 and GLM-5.3 with a 35B MoE backbone
- Windowed Interaction RL (WIRL): assigns shared outcome rewards to multi-turn windows between successive experiment results with asynchronous policy updates
- Cross-domain AI4AI generalization: transfers optimization skills directly to NatureBench Lite, agent scaffold optimization, and algorithm engineering
Linked Models & Agents:
