Building generalist agents that can rapidly adapt to new environments is a key challenge for deploying AI in the digital and real worlds. Is scaling current agent architectures the most effective way to build generalist agents? We propose a novel approach to pre-train relatively small policies on relatively small datasets and adapt them to unseen environments via in-context learning, without any finetuning. Our key idea is that retrieval offers a powerful bias for fast adaptation. Indeed, we demonstrate that even a simple retrieval-based 1-nearest neighbor agent offers a surprisingly strong baseline for today's state-of-the-art generalist agents. From this starting point, we construct a semi-parametric agent, REGENT, that trains a transformer-based policy on sequences of queries and retrieved neighbors. REGENT can generalize to unseen robotics and game-playing environments via retrieval augmentation and in-context learning, achieving this with up to 3x fewer parameters and up to an order-of-magnitude fewer pre-training datapoints, significantly outperforming today's state-of-the-art generalist agents. Website: https://kaustubhsridhar.github.io/regent-research

翻译：构建能够快速适应新环境的通用智能体，是在数字和现实世界中部署人工智能的关键挑战。扩大现有智能体架构的规模是构建通用智能体的最有效途径吗？我们提出了一种新颖的方法：在相对较小的数据集上预训练相对较小的策略模型，并通过上下文学习使其适应未见环境，无需任何微调。我们的核心思想是：检索为快速适应提供了强大的偏置引导。事实上，我们证明即使是一个简单的基于检索的1-最近邻智能体，也能为当前最先进的通用智能体提供一个令人惊讶的强基线。基于此起点，我们构建了一个半参数化智能体REGENT，它在查询序列和检索到的邻近样本序列上训练基于Transformer的策略。REGENT能够通过检索增强和上下文学习，泛化到未见过的机器人和游戏环境中，且实现这一目标所需的参数量最多可减少3倍，预训练数据点最多可减少一个数量级，其性能显著优于当前最先进的通用智能体。项目网站：https://kaustubhsridhar.github.io/regent-research