WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World
  Model-based LLM Agents
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.
6808579f91ba7dbcc19dbd3e
Siyu Zhou
claimed_verified
2025-04-24T09:11:06.138Z
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/avatars/ab8f3bc0844b02c5e26a450bc3ee7689.svg
Siyu Zhou
user
Sawyer99
6808579f91ba7dbcc19dbd3f
Tianyi Zhou
claimed_verified
2025-04-23T08:27:49.077Z
647f5af5b0e96764589f3b2a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/VJ4cDyjp5M3V5WmI5gPIU.jpeg
Tianyi Zhou
user
zhoutianyi
6808579f91ba7dbcc19dbd40
Yijun Yang
6808579f91ba7dbcc19dbd41
Guodong Long
6808579f91ba7dbcc19dbd42
Deheng Ye
6808579f91ba7dbcc19dbd43
Jing Jiang
6808579f91ba7dbcc19dbd44
Chengqi Zhang
admin_assigned
2025-04-23T13:55:38.417Z
64bc61b0f671da974e15dfa4
/avatars/3f4d830c23ad6a035176e4c2ece11b3e.svg
Chengqi Zhang
user
PiscesQK
WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World
  Model-based LLM Agents
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.
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.
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