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Ricardo-H/BehR-WorldModel-Webshop-Qwen2.5-7B

sourceHugging Faceotherupdated 28d agoView on Hugging Face
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BehR-WorldModel-Webshop-Qwen2.5-7B

A behavior-consistent text-based world model for WebShop.

Model details

FieldValue
Base model`X1AOX1A/WorldModel-Webshop-Qwen2.5-7B`
Base revision99e530d0e6f19c1e61734b61eaec8c2a3a11cabd
ArchitectureQwen2ForCausalLM (Qwen2.5-7B)
EnvironmentWebShop, via AgentGym

Given the agent-visible interaction history and the agent's next action, the model predicts the next environment observation. It can therefore act as a text simulator for agent rollouts in WebShop.

Transformers usage

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Ricardo-H/BehR-WorldModel-Webshop-Qwen2.5-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

Evaluation

The paper evaluates this model on WebShop with single-step exact match, task-success and consistency metrics. See the paper and the serving and evaluation repository for the evaluation protocol and reported results.

Intended use and limitations

  • —Intended for research on world models and agent simulation in text-based environments.
  • —Evaluated only on WebShop; behavior outside that distribution is unvalidated.
  • —Predicted observations may be plausible but factually wrong. Do not treat outputs as ground truth about the real environment.
  • —The model inherits biases and failure modes from Qwen2.5-7B and the base world model.

License and provenance

This model is a derivative of `X1AOX1A/WorldModel-Webshop-Qwen2.5-7B` at revision 99e530d0e6f19c1e61734b61eaec8c2a3a11cabd, which is itself derived from Qwen2.5-7B. Use is governed by the Qwen2.5 license and by the terms of the base world model; check the base repository before redistribution or commercial use. The serving and evaluation repository is Apache-2.0; these model weights are not.

The base world model and the WebShop/TextWorld data splits originate from From Word to World: Can Large Language Models be Implicit Text-based World Models? (arXiv:2512.18832).

Citation

bibtex
@article{huang2026behrwm,
  title   = {Beyond State Consistency: Behavior Consistency in Text-Based World Models},
  author  = {Huang, Youling and Chen, Guanqiao and Yao, Junchi and Wang, Lu and
             Yang, Fangkai and Du, Chao and Zhao, ChenZhuo and Zhao, Pu and
             Lin, Qingwei and Rajmohan, Saravan and Zhang, Dongmei},
  journal = {arXiv preprint arXiv:2604.13824},
  year    = {2026},
  url     = {https://arxiv.org/abs/2604.13824}
}