Generating Symbolic World Models via Test-time Scaling of Large Language
  Models
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.
67a97d1c02da0cdf059cb0d8
Zhouliang Yu
admin_assigned
2025-02-10T16:16:00.654Z
62a80fe3ac97233f1625235a
https://cdn-avatars.huggingface.co/v1/production/uploads/62a80fe3ac97233f1625235a/_rGtpqdY7OEBz3pyqb6fE.jpeg
Zhouliang Yu
user
zhouliang
67a97d1c02da0cdf059cb0d9
Yuhuan Yuan
admin_assigned
2025-02-10T16:16:06.566Z
632ff46bf242a8532b713381
/avatars/72e96e0dd7d7b4fce64a07def170174f.svg
yuhuanyuan
user
yuhuanyuan
67a97d1c02da0cdf059cb0da
Tim Z. Xiao
67a97d1c02da0cdf059cb0db
Fuxiang Frank Xia
67a97d1c02da0cdf059cb0dc
Jie Fu
claimed_verified
2025-02-10T13:12:00.097Z
641a6895fb5ffff5ac79d593
https://cdn-avatars.huggingface.co/v1/production/uploads/641a6895fb5ffff5ac79d593/vxvwsto3llOEWGqQKGMYx.jpeg
Jie Fu
user
bigaidream
67a97d1c02da0cdf059cb0dd
Ge Zhang
claimed_verified
2025-02-10T09:49:56.250Z
638efcf4c67af472d316d424
/avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg
Ge Zhang
user
zhangysk
67a97d1c02da0cdf059cb0de
Ge Lin
67a97d1c02da0cdf059cb0df
Weiyang Liu
admin_assigned
2025-02-10T16:16:29.068Z
648905d1a15c43c791d4381f
https://cdn-avatars.huggingface.co/v1/production/uploads/648905d1a15c43c791d4381f/GpqGBzsLiMHX0gWZEz3qn.jpeg
Weiyang Liu
user
wy1iu
Generating Symbolic World Models via Test-time Scaling of Large Language
  Models
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.
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.
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