title: Large Language Models as General Pattern Machines
summary: 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.
paper_authors._id: 64acb42a5c6fc404f0d526a6
paper_authors.name: Suvir Mirchandani
paper_authors._id: 64acb42a5c6fc404f0d526a7
paper_authors.name: Fei Xia
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-23T08:10:49.554Z
paper_authors._id: 62cfa9ee9b96f22525c36d47
paper_authors.avatarUrl: /avatars/8bfe3bf63f0609b3b6b7d7c5d7f6b3e9.svg
paper_authors.fullname: Fei Xia
paper_authors.type: user
paper_authors.user: fxia22
paper_authors._id: 64acb42a5c6fc404f0d526a8
paper_authors.name: Pete Florence
paper_authors._id: 64acb42a5c6fc404f0d526a9
paper_authors.name: Brian Ichter
paper_authors._id: 64acb42a5c6fc404f0d526aa
paper_authors.name: Danny Driess
paper_authors._id: 64acb42a5c6fc404f0d526ab
paper_authors.name: Montserrat Gonzalez Arenas
paper_authors._id: 64acb42a5c6fc404f0d526ac
paper_authors.name: Kanishka Rao
paper_authors._id: 64acb42a5c6fc404f0d526ad
paper_authors.name: Dorsa Sadigh
paper_authors._id: 64acb42a5c6fc404f0d526ae
paper_authors.name: Andy Zeng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-12T07:29:45.271Z
paper_authors._id: 62d5f9d705461e9459d20b91
paper_authors.avatarUrl: /avatars/120917da2d8587674505163fb960e27d.svg
paper_authors.fullname: Andy Zeng
paper_authors.type: user
paper_authors.user: andyzeng
paper_title: Large Language Models as General Pattern Machines
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) autoregressively completing token sequences probabilistic context-free grammars (PCFG) Abstract Reasoning Corpus (ARC) ASCII art random sampling general sequence models in-context learning robotics numerical sequences simple motions reward-conditioned trajectories closed-loop policies stabilizing controller CartPole