Large Language Models as General Pattern Machines
Authors: name: Suvir Mirchandani, name: Fei Xia, name: Pete Florence, name: Brian Ichter, name: Danny Driess, name: Montserrat Gonzalez Arenas, name: Kanishka Rao, name: Dorsa Sadigh, name: Andy Zeng

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 Abstract 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.