MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering
[Sztuczna inteligencja] Autorzy: Shangheng Du, Jinxin Shi, Shuaiyu Zhang i 7 więcej
Large language models (LLMs) are expanding from assisting scientific research and engineering to autonomously developing and improving AI systems, advancing AI for AI (AI4AI) as a pathway toward recursive self-improvement (RSI). Workflow-driven machine learning engineering (MLE) systems provide a practical path toward this pursuit through complex orchestration that manages long-horizon experimentation, while recent MLE systems adopt model-directed interaction, allowing models to autonomously organize experiments and adapt optimization strategies based on execution feedback. However, feedback from each experiment reflects several preceding decisions, and a long MLE run contains many such costly experiments. Training must therefore select useful supervision from long interaction trajectories and learn from intermediate outcomes. We propose a post-training method for autonomous long-horizo…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja