用检索增强实现小模型快速适应新环境，性能超越现有大模型。
REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments
- 通过检索邻居样本，让小模型在不微调下实现上下文自适应。
- 参数量少3倍、预训练数据少10倍，仍优于当前最强通用智能体。
- 适合研究高效智能体或资源受限场景下的快速部署应用。
构建能快速适应新环境的通用智能体是人工智能在数字与现实世界部署的关键挑战。当前智能体架构是否需要持续扩大规模？我们提出一种新方法：在较小数据集上预训练小型策略，并通过上下文学习在无微调情况下适配未见环境。核心思想是：检索提供强大的快速适应先验。实验证明，即使简单的1-最近邻检索基线也远超当前主流通用智能体。在此基础上，我们构建了半参数化智能体REGENT，其基于Transformer的策略网络处理查询与检索邻居序列。REGENT通过检索增强与上下文学习，成功泛化至未见过的机器人和游戏环境，参数量最多减少3倍，预训练数据最多减少一个数量级，显著优于现有最先进通用智能体。
原文摘要 · Abstract (English)
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
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