通过元学习代理记忆设计实现持续学习
Learning to Continually Learn via Meta-learning Agentic Memory Designs
AI总结：
本文提出ALMA框架，通过元学习自动设计记忆模块，使代理系统在多样化领域实现持续学习。
摘要：
基础模型的状态lessness阻碍了代理系统持续学习的能力，这是长期推理和适应的核心能力。为了解决这一限制，代理系统通常会加入记忆模块以保留和重用过去的经验，旨在测试期间实现持续学习。然而，大多数现有记忆设计都是人工设计且固定的，这限制了它们适应现实世界任务多样性和非平稳性的能力。在本文中，我们介绍了ALMA（用于代理系统的自动记忆设计元学习框架），该框架通过元学习记忆设计来替代人工设计的记忆设计，从而减少人为努力，使代理系统能够在多样化领域成为持续学习者。我们的方法采用一个元代理，在开放式的范围内搜索以可执行代码表达的记忆设计，理论上允许发现任意的记忆设计，包括数据库模式以及它们的检索和更新机制。在四个连续决策领域中进行的广泛实验表明，所学的记忆设计在所有基准上都比最先进的手工设计的记忆设计更有效和高效。当安全开发和部署时，ALMA代表了朝着能够学习成为适应性和持续学习者的自我改进人工智能系统迈出的一步。
英文摘要：
The statelessness of foundation models bottlenecks agentic systems' ability to continually learn, a core capability for long-horizon reasoning and adaptation. To address this limitation, agentic systems commonly incorporate memory modules to retain and reuse past experience, aiming for continual learning during test time. However, most existing memory designs are human-crafted and fixed, which limits their ability to adapt to the diversity and non-stationarity of real-world tasks. In this paper, we introduce ALMA (Automated meta-Learning of Memory designs for Agentic systems), a framework that meta-learns memory designs to replace hand-engineered memory designs, therefore minimizing human effort and enabling agentic systems to be continual learners across diverse domains. Our approach employs a Meta Agent that searches over memory designs expressed as executable code in an open-ended manner, theoretically allowing the discovery of arbitrary memory designs, including database schemas as well as their retrieval and update mechanisms. Extensive experiments across four sequential decision-making domains demonstrate that the learned memory designs enable more effective and efficient learning from experience than state-of-the-art human-crafted memory designs on all benchmarks. When developed and deployed safely, ALMA represents a step toward self-improving AI systems that learn to be adaptive, continual learners.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
