Core Viewpoint - The article discusses the emergence of AI-for-AI (AI4AI) as a solution to the limitations of traditional AI development, which heavily relies on human intervention and manual tuning, thereby slowing down innovation and the path to Artificial General Intelligence (AGI) [1][6]. Group 1: AI4AI Development - AI4AI aims to enable AI agents to autonomously design, optimize, and improve AI algorithms, significantly reducing human involvement and accelerating the iterative development cycle [1][6]. - A recent study by Shanghai Jiao Tong University and Shanghai AI Lab demonstrated that a 7 billion parameter AI agent (ML-Agent) could surpass a 671 billion parameter model (Deepseek-R1) in performance by utilizing a new paradigm of "experience learning" [2][9]. Group 2: Traditional Machine Learning Challenges - Traditional machine learning processes are time-consuming and inefficient, often requiring days to months for model design and parameter tuning, which limits the speed of AI innovation [4][5]. - Existing AI agents still depend on human-designed prompts, leading to a cycle of waiting, modifying, and retrying, which perpetuates inefficiency [5][6]. Group 3: Breakthroughs in Autonomous Machine Learning - The study introduces a learning-based paradigm for autonomous machine learning, allowing agents to learn from execution trajectories through online reinforcement learning, enabling proactive exploration of strategies [7][9]. - The ML-Agent, powered by a 7 billion parameter model, demonstrated remarkable performance improvements by learning from just nine machine learning tasks, showcasing its ability to generalize across tasks [20][24]. Group 4: Training Framework and Methodologies - The training framework includes three core breakthroughs that enhance the self-evolution of AI agents, such as exploration-enriched fine-tuning and a step-wise reinforcement learning paradigm [11][15]. - A customized reward module was developed to unify feedback from complex experimental results, providing consistent signals for reinforcement learning optimization [19][20]. Group 5: Performance Comparison and Results - ML-Agent outperformed several advanced AI models in both seen and unseen machine learning tasks, demonstrating its strong generalization capabilities [20][22]. - The research highlights that ML-Agent's performance consistently improved throughout training, surpassing all baseline methods and establishing a new paradigm for AI design [24][25]. Group 6: Community and Future Directions - ML-Agent is part of the MASWorks open-source community, which aims to connect global researchers and foster collaboration in the multi-agent systems field [26][27]. - The community plans to host a workshop focused on large language models and multi-agent systems at ICML 2025, encouraging participation from scholars worldwide [28].