从 ReasoningBank 到 MetaAgent，RL 未必是 Agent 自进化的必要解？

Core Viewpoint - The article discusses the evolution of intelligent agents, emphasizing the importance of memory systems in enabling self-evolution beyond traditional reinforcement learning (RL) methods. It highlights the exploration of various technical directions, including metacognition and self-diagnosis, to enhance the capabilities of intelligent agents. Group 1: Memory Systems and Their Evolution - Recent advancements in artificial intelligence have shifted focus from solely large language models to self-evolving intelligent agents capable of executing complex tasks in dynamic environments [4] - The development of memory systems aims to transform immediate reasoning into cumulative, transferable long-term experiences, allowing agents to remember not just what to think but how to think [7][8] - The evolution of memory systems is categorized into three stages: No Memory Agent, Trajectory Memory, and Workflow Memory, each with its limitations regarding knowledge abstraction and adaptability [8][9] Group 2: ReasoningBank Mechanism - The ReasoningBank mechanism aims to elevate the abstraction level of agent memory from operational records to generalized reasoning strategies, enhancing knowledge readability and transferability across tasks [10] - It operates on a self-aware feedback loop that includes memory retrieval, construction, and integration, facilitating a closed-loop learning process without external supervision [7][10] - The Memory-aware Test-Time Scaling (MaTTS) mechanism optimizes resource allocation to enhance the quality of comparative signals, leading to improved reasoning strategies and faster adaptive evolution of agents [11][12] Group 3: Future Directions in Self-Evolution - While memory system improvements are currently the mainstream approach for enabling self-evolution in AI, researchers are also exploring other technical routes, such as self-recognition and external tool assistance [14]