通过元学习智能记忆设计实现持续学习能力

摘要

基础模型的无状态特性制约了智能体系统持续学习的能力，而这一能力正是实现长程推理与适应的核心。为突破此限制，智能体系统通常通过引入记忆模块来保存和复用过往经验，以期在测试阶段实现持续学习。然而现有记忆设计大多依赖人工构建且结构固定，难以适应现实任务中的多样性与非平稳性特征。本文提出ALMA（面向智能体系统的自动化记忆设计元学习框架），该框架通过元学习生成记忆设计以替代人工工程方案，从而最大限度减少人力投入，使智能体系统成为跨领域持续学习者。我们的方法采用元代理对可执行代码形式的记忆设计进行开放式搜索，理论上能够发现任意记忆设计方案（包括数据库模式及其检索更新机制）。在四个序列决策领域的广泛实验表明，相较于所有基准测试中最先进的人工设计记忆方案，通过ALMA习得的记忆设计能实现更高效的经验学习。在安全开发部署的前提下，ALMA标志着人工智能系统向自我改进方向迈出重要一步——这类系统能够学会成为具有适应性的持续学习者。

English

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.

通过元学习智能记忆设计实现持续学习能力

Learning to Continually Learn via Meta-learning Agentic Memory Designs

摘要

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