We observe that pre-trained large language models (LLMs) are capable of autoregressively completing complex token sequences -- from arbitrary ones procedurally generated by probabilistic context-free grammars (PCFG), to more rich spatial patterns found in the Abstraction and Reasoning Corpus (ARC), a general AI benchmark, prompted in the style of ASCII art. Surprisingly, pattern completion proficiency can be partially retained even when the sequences are expressed using tokens randomly sampled from the vocabulary. These results suggest that without any additional training, LLMs can serve as general sequence modelers, driven by in-context learning. In this work, we investigate how these zero-shot capabilities may be applied to problems in robotics -- from extrapolating sequences of numbers that represent states over time to complete simple motions, to least-to-most prompting of reward-conditioned trajectories that can discover and represent closed-loop policies (e.g., a stabilizing controller for CartPole). While difficult to deploy today for real systems due to latency, context size limitations, and compute costs, the approach of using LLMs to drive low-level control may provide an exciting glimpse into how the patterns among words could be transferred to actions.

翻译：我们观察到，预训练的大语言模型（LLMs）能够自回归地完成复杂的标记序列——从由概率上下文无关文法（PCFG）程序化生成的任意序列，到以ASCII艺术风格提示的抽象推理语料库（ARC，一种通用AI基准测试）中更丰富的空间模式。令人惊讶的是，即使序列使用从词汇表中随机采样的标记表示，模式完成能力也可以部分保留。这些结果表明，无需额外训练，LLMs即可作为通用序列建模器，由上下文学习驱动。在本工作中，我们研究了这些零样本能力如何应用于机器人学问题——从外推表示随时间变化的状态数值序列以完成简单运动，到最小到最大提示奖励条件轨迹以发现并表征闭环策略（例如，用于CartPole的稳定控制器）。尽管由于延迟、上下文大小限制和计算成本，当前难以部署于实际系统，但使用LLMs驱动低级控制的方法或许能令人兴奋地一瞥词汇模式如何转移至行动。