title: Self-Steering Language Models
summary: While test-time reasoning enables language models to tackle complex tasks,
  searching or planning in natural language can be slow, costly, and error-prone.
  But even when LMs struggle to emulate the precise reasoning steps needed to
  solve a problem, they often excel at describing its abstract structure--both
  how to verify solutions and how to search for them. This paper introduces
  DisCIPL, a method for "self-steering" LMs where a Planner model generates a
  task-specific inference program that is executed by a population of Follower
  models. Our approach equips LMs with the ability to write recursive search
  procedures that guide LM inference, enabling new forms of verifiable and
  efficient reasoning. When instantiated with a small Follower (e.g.,
  Llama-3.2-1B), DisCIPL matches (and sometimes outperforms) much larger models,
  including GPT-4o and o1, on challenging constrained generation tasks. In
  decoupling planning from execution, our work opens up a design space of
  highly-parallelized Monte Carlo inference strategies that outperform standard
  best-of-N sampling, require no finetuning, and can be implemented automatically
  by existing LMs.
paper_authors._id: 67f7823da630bcdabbd8b3eb
paper_authors.name: Gabriel Grand
paper_authors._id: 67f7823da630bcdabbd8b3ec
paper_authors.name: Joshua B. Tenenbaum
paper_authors._id: 67f7823da630bcdabbd8b3ed
paper_authors.name: Vikash K. Mansinghka
paper_authors._id: 67f7823da630bcdabbd8b3ee
paper_authors.name: Alexander K. Lew
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-04-10T08:33:02.433Z
paper_authors._id: 673cc08370644bb836283fec
paper_authors.avatarUrl: /avatars/b8c7f1a10ddf76dcd06398c59f553b61.svg
paper_authors.fullname: Alexander Lew
paper_authors.type: user
paper_authors.user: alexanderlew
paper_authors._id: 67f7823da630bcdabbd8b3ef
paper_authors.name: Jacob Andreas
paper_title: Self-Steering Language Models
paper_summary: While test-time reasoning enables language models to tackle complex tasks,
  searching or planning in natural language can be slow, costly, and error-prone.
  But even when LMs struggle to emulate the precise reasoning steps needed to
  solve a problem, they often excel at describing its abstract structure--both
  how to verify solutions and how to search for them. This paper introduces
  DisCIPL, a method for "self-steering" LMs where a Planner model generates a
  task-specific inference program that is executed by a population of Follower
  models. Our approach equips LMs with the ability to write recursive search
  procedures that guide LM inference, enabling new forms of verifiable and
  efficient reasoning. When instantiated with a small Follower (e.g.,
  Llama-3.2-1B), DisCIPL matches (and sometimes outperforms) much larger models,
  including GPT-4o and o1, on challenging constrained generation tasks. In
  decoupling planning from execution, our work opens up a design space of
  highly-parallelized Monte Carlo inference strategies that outperform standard
  best-of-N sampling, require no finetuning, and can be implemented automatically
  by existing LMs.
paper_ai_summary: DisCIPL, a method combining a Planner and Follower model, enables efficient and verifiable reasoning in language models by generating task-specific inference programs.
paper_ai_keywords: test-time reasoning language models Planner model Follower models recursive search procedures verifiable reasoning constrained generation tasks Monte Carlo inference strategies best-of-N sampling existing LMs