title: WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World
    Model-based LLM Agents
summary: Can we build accurate world models out of large language models (LLMs)? How
  can world models benefit LLM agents? The gap between the prior knowledge of
  LLMs and the specified environment's dynamics usually bottlenecks LLMs'
  performance as world models. To bridge the gap, we propose a training-free
  "world alignment" that learns an environment's symbolic knowledge complementary
  to LLMs. The symbolic knowledge covers action rules, knowledge graphs, and
  scene graphs, which are extracted by LLMs from exploration trajectories and
  encoded into executable codes to regulate LLM agents' policies. We further
  propose an RL-free, model-based agent "WALL-E 2.0" through the model-predictive
  control (MPC) framework. Unlike classical MPC requiring costly optimization on
  the fly, we adopt an LLM agent as an efficient look-ahead optimizer of future
  steps' actions by interacting with the neurosymbolic world model. While the LLM
  agent's strong heuristics make it an efficient planner in MPC, the quality of
  its planned actions is also secured by the accurate predictions of the aligned
  world model. They together considerably improve learning efficiency in a new
  environment. On open-world challenges in Mars (Minecraft like) and ALFWorld
  (embodied indoor environments), WALL-E 2.0 significantly outperforms existing
  methods, e.g., surpassing baselines in Mars by 16.1%-51.6% of success rate and
  by at least 61.7% in score. In ALFWorld, it achieves a new record 98% success
  rate after only 4 iterations.
paper_authors._id: 6808579f91ba7dbcc19dbd3e
paper_authors.name: Siyu Zhou
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/ab8f3bc0844b02c5e26a450bc3ee7689.svg
paper_authors.fullname: Siyu Zhou
paper_authors.type: user
paper_authors.user: Sawyer99
paper_authors._id: 6808579f91ba7dbcc19dbd3f
paper_authors.name: Tianyi Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-23T08:27:49.077Z
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paper_authors.fullname: Tianyi Zhou
paper_authors.type: user
paper_authors.user: zhoutianyi
paper_authors._id: 6808579f91ba7dbcc19dbd40
paper_authors.name: Yijun Yang
paper_authors._id: 6808579f91ba7dbcc19dbd41
paper_authors.name: Guodong Long
paper_authors._id: 6808579f91ba7dbcc19dbd42
paper_authors.name: Deheng Ye
paper_authors._id: 6808579f91ba7dbcc19dbd43
paper_authors.name: Jing Jiang
paper_authors._id: 6808579f91ba7dbcc19dbd44
paper_authors.name: Chengqi Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-23T13:55:38.417Z
paper_authors._id: 64bc61b0f671da974e15dfa4
paper_authors.avatarUrl: /avatars/3f4d830c23ad6a035176e4c2ece11b3e.svg
paper_authors.fullname: Chengqi Zhang
paper_authors.type: user
paper_authors.user: PiscesQK
paper_title: WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World
    Model-based LLM Agents
paper_summary: Can we build accurate world models out of large language models (LLMs)? How
  can world models benefit LLM agents? The gap between the prior knowledge of
  LLMs and the specified environment's dynamics usually bottlenecks LLMs'
  performance as world models. To bridge the gap, we propose a training-free
  "world alignment" that learns an environment's symbolic knowledge complementary
  to LLMs. The symbolic knowledge covers action rules, knowledge graphs, and
  scene graphs, which are extracted by LLMs from exploration trajectories and
  encoded into executable codes to regulate LLM agents' policies. We further
  propose an RL-free, model-based agent "WALL-E 2.0" through the model-predictive
  control (MPC) framework. Unlike classical MPC requiring costly optimization on
  the fly, we adopt an LLM agent as an efficient look-ahead optimizer of future
  steps' actions by interacting with the neurosymbolic world model. While the LLM
  agent's strong heuristics make it an efficient planner in MPC, the quality of
  its planned actions is also secured by the accurate predictions of the aligned
  world model. They together considerably improve learning efficiency in a new
  environment. On open-world challenges in Mars (Minecraft like) and ALFWorld
  (embodied indoor environments), WALL-E 2.0 significantly outperforms existing
  methods, e.g., surpassing baselines in Mars by 16.1%-51.6% of success rate and
  by at least 61.7% in score. In ALFWorld, it achieves a new record 98% success
  rate after only 4 iterations.
paper_ai_summary: World alignment and model-based agent WALL-E 2.0 enhance LLMs' performance as world models in new environments by integrating symbolic knowledge and efficient planning.
paper_ai_keywords: world models large language models (LLMs) symbolic knowledge knowledge graphs scene graphs exploration trajectories executable codes RL-free model-predictive control (MPC) neurosymbolic world model efficient look-ahead optimizer future steps' actions learning efficiency success rate score open-world challenges Mars ALFWorld