title: Generating Symbolic World Models via Test-time Scaling of Large Language
    Models
summary: Solving complex planning problems requires Large Language Models (LLMs) to
  explicitly model the state transition to avoid rule violations, comply with
  constraints, and ensure optimality-a task hindered by the inherent ambiguity of
  natural language. To overcome such ambiguity, Planning Domain Definition
  Language (PDDL) is leveraged as a planning abstraction that enables precise and
  formal state descriptions. With PDDL, we can generate a symbolic world model
  where classic searching algorithms, such as A*, can be seamlessly applied to
  find optimal plans. However, directly generating PDDL domains with current LLMs
  remains an open challenge due to the lack of PDDL training data. To address
  this challenge, we propose to scale up the test-time computation of LLMs to
  enhance their PDDL reasoning capabilities, thereby enabling the generation of
  high-quality PDDL domains. Specifically, we introduce a simple yet effective
  algorithm, which first employs a Best-of-N sampling approach to improve the
  quality of the initial solution and then refines the solution in a fine-grained
  manner with verbalized machine learning. Our method outperforms o1-mini by a
  considerable margin in the generation of PDDL domain, achieving over 50%
  success rate on two tasks (i.e., generating PDDL domains from natural language
  description or PDDL problems). This is done without requiring additional
  training. By taking advantage of PDDL as state abstraction, our method is able
  to outperform current state-of-the-art methods on almost all competition-level
  planning tasks.
paper_authors._id: 67a97d1c02da0cdf059cb0d8
paper_authors.name: Zhouliang Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-10T16:16:00.654Z
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paper_authors.fullname: Zhouliang Yu
paper_authors.type: user
paper_authors.user: zhouliang
paper_authors._id: 67a97d1c02da0cdf059cb0d9
paper_authors.name: Yuhuan Yuan
paper_authors.status: admin_assigned
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paper_authors._id: 632ff46bf242a8532b713381
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paper_authors.fullname: yuhuanyuan
paper_authors.type: user
paper_authors.user: yuhuanyuan
paper_authors._id: 67a97d1c02da0cdf059cb0da
paper_authors.name: Tim Z. Xiao
paper_authors._id: 67a97d1c02da0cdf059cb0db
paper_authors.name: Fuxiang Frank Xia
paper_authors._id: 67a97d1c02da0cdf059cb0dc
paper_authors.name: Jie Fu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-10T13:12:00.097Z
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paper_authors.fullname: Jie Fu
paper_authors.type: user
paper_authors.user: bigaidream
paper_authors._id: 67a97d1c02da0cdf059cb0dd
paper_authors.name: Ge Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-10T09:49:56.250Z
paper_authors._id: 638efcf4c67af472d316d424
paper_authors.avatarUrl: /avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg
paper_authors.fullname: Ge Zhang
paper_authors.type: user
paper_authors.user: zhangysk
paper_authors._id: 67a97d1c02da0cdf059cb0de
paper_authors.name: Ge Lin
paper_authors._id: 67a97d1c02da0cdf059cb0df
paper_authors.name: Weiyang Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-10T16:16:29.068Z
paper_authors._id: 648905d1a15c43c791d4381f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/648905d1a15c43c791d4381f/GpqGBzsLiMHX0gWZEz3qn.jpeg
paper_authors.fullname: Weiyang Liu
paper_authors.type: user
paper_authors.user: wy1iu
paper_title: Generating Symbolic World Models via Test-time Scaling of Large Language
    Models
paper_summary: Solving complex planning problems requires Large Language Models (LLMs) to
  explicitly model the state transition to avoid rule violations, comply with
  constraints, and ensure optimality-a task hindered by the inherent ambiguity of
  natural language. To overcome such ambiguity, Planning Domain Definition
  Language (PDDL) is leveraged as a planning abstraction that enables precise and
  formal state descriptions. With PDDL, we can generate a symbolic world model
  where classic searching algorithms, such as A*, can be seamlessly applied to
  find optimal plans. However, directly generating PDDL domains with current LLMs
  remains an open challenge due to the lack of PDDL training data. To address
  this challenge, we propose to scale up the test-time computation of LLMs to
  enhance their PDDL reasoning capabilities, thereby enabling the generation of
  high-quality PDDL domains. Specifically, we introduce a simple yet effective
  algorithm, which first employs a Best-of-N sampling approach to improve the
  quality of the initial solution and then refines the solution in a fine-grained
  manner with verbalized machine learning. Our method outperforms o1-mini by a
  considerable margin in the generation of PDDL domain, achieving over 50%
  success rate on two tasks (i.e., generating PDDL domains from natural language
  description or PDDL problems). This is done without requiring additional
  training. By taking advantage of PDDL as state abstraction, our method is able
  to outperform current state-of-the-art methods on almost all competition-level
  planning tasks.
paper_ai_summary: A novel algorithm enhances Large Language Models' PDDL reasoning by employing Best-of-N sampling and verbalized machine learning, significantly improving PDDL domain generation from natural language.
paper_ai_keywords: Large Language Models LLMs Planning Domain Definition Language PDDL state transition rule violations constraints ambiguity symbolic world model A* Best-of-N sampling verbalized machine learning success rate competition-level planning tasks