Large language model (LLM)-based decision-making agents have shown the
ability to generalize across multiple tasks. However, their performance relies
on massive data and compute. We argue that this inefficiency stems from the
forgetting phenomenon, in which a model memorizes its behaviors in parameters
throughout training. As a result, training on a new task may deteriorate the
model's performance on previous tasks. In contrast to LLMs' implicit memory
mechanism, the human brain utilizes distributed memory storage, which helps
manage and organize multiple skills efficiently, mitigating the forgetting
phenomenon. Thus inspired, we propose an internal working memory module to
store, blend, and retrieve information for different downstream tasks.
Evaluation results show that the proposed method improves training efficiency
and generalization in both Atari games and meta-world object manipulation
tasks. Moreover, we demonstrate that memory fine-tuning further enhances the
adaptability of the proposed architecture.

本文提出了一种基于内部工作记忆模块的决策制定代理，可以通过存储、混合和检索信息来改善其在不同下游任务中的训练效率和泛化能力，并进一步证明记忆微调可以增强所提出架构的适应性。