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[!Note] This repository contains the model weights and configuration files for Qwen-AgentWorld-35B-A3B, a native language world model trained for agentic environment simulation.
These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc.
Qwen-AgentWorld is the first language world model to cover seven agent interaction domains within a single model. It simulates agentic environments via long chain-of-thought reasoning, predicting the next environment state given an agent's action and interaction history. Trained through a three-stage pipeline β CPT injects environment knowledge, SFT activates next-state-prediction reasoning, RL sharpens simulation fidelity β Qwen-AgentWorld is a native world model: environment modeling is the training objective from the CPT stage onward, not a post-hoc add-on.
Five-dimensional rubric mean per domain, normalized to 0-100 scale.
Qwen-AgentWorld-35B-A3B can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-compatible API servers.
[!Important] The model has a default context length of 262,144 tokens. If you encounter out-of-memory (OOM) errors, consider reducing the context window. However, because Qwen-AgentWorld leverages extended context for multi-turn environment simulation, we advise maintaining a context length of at least 128K tokens.
SGLang is a fast serving framework for large language models.
An OpenAI-compatible API will be available at http://localhost:8000/v1.
vLLM is a high-throughput and memory-efficient inference engine for LLMs.
An OpenAI-compatible API will be available at http://localhost:8000/v1.
[!Note] We provide domain-specific world model system prompt templates in
prompts/of the GitHub repository for all 7 domains. These serve as general-purpose system prompts when using Qwen-AgentWorld as an environment simulator. Each domain folder contains asystem_prompt.txt(world model system prompt) and ajudge_system_prompt.txt(evaluation prompt).
AgentWorldBench evaluates language world models by scoring each predicted environment observation on 5 dimensions: Format, Factuality, Consistency, Realism, and Quality.
The evaluation follows a three-step pipeline:
Sampling Parameters: We recommend temperature=0.6, top_p=0.95, top_k=20 for world model inference. The model uses thinking mode by default (<think>...</think>) to reason about environment state transitions before producing the predicted observation.
Adequate Output Length: We recommend an output length of 32,768 tokens for most queries. For long, multi-step trajectories, you may increase the max output length to accommodate detailed environment observations.
Domain-Specific System Prompts: For optimal simulation fidelity, use the domain-specific system prompts provided in the prompts/ directory of the GitHub repository.
If you find our work helpful, feel free to give us a cite.
python -m sglang.launch_server \ --model-path Qwen/Qwen-AgentWorld-35B-A3B \ --port 8000 \ --tp-size 4 \ --context-length 262144 \ --reasoning-parser qwen3vllm serve Qwen/Qwen-AgentWorld-35B-A3B \ --port 8000 \ --tensor-parallel-size 4 \ --max-model-len 262144 \ --reasoning-parser qwen3 \ --trust-remote-codefrom transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen-AgentWorld-35B-A3B"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto",)
messages = [ { "role": "system", "content": "You are a language world model simulating a Linux terminal environment. " "Given the user's command, predict the terminal output." }, { "role": "user", "content": "Action: execute_bash\nCommand: ls -la /home/user/project/" }]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer([text], return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.6)response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)print(response)from openai import OpenAI
client = OpenAI( base_url="http://localhost:8000/v1", api_key="EMPTY",)
# Terminal domain examplemessages = [ { "role": "system", "content": "You are a language world model simulating a Linux terminal environment. " "Given the user's command, predict the terminal output." }, { "role": "user", "content": "Action: execute_bash\nCommand: ls -la /home/user/project/" }]
response = client.chat.completions.create( model="Qwen/Qwen-AgentWorld-35B-A3B", messages=messages, max_tokens=32768, temperature=0.6,)print(response.choices[0].message.content)# Clone the evaluation repositorygit clone https://github.com/QwenLM/Qwen-AgentWorld.gitcd Qwen-AgentWorld
# Download the benchmarkhuggingface-cli download Qwen/AgentWorldBench --repo-type dataset --local-dir ./AgentWorldBench
# Install dependenciespip install openaicd eval
# Step 1: Run world model inferencepython eval.py infer \ --data-dir ../AgentWorldBench \ --model-base-url http://localhost:8000/v1 \ --model-name Qwen/Qwen-AgentWorld-35B-A3B \ --output-dir ./results
# Step 2: Run LLM judge scoringexport OPENAI_API_KEY="your-api-key"python eval.py judge \ --predictions ./results/predictions.jsonl \ --judge-base-url https://api.openai.com/v1 \ --judge-model gpt-5.2-2025-12-11 \ --output-dir ./results
# Step 3: Aggregate and display scorespython eval.py score --predictions ./results/judged.jsonl@article{zuo2026qwen, title={Qwen-agentworld: language world models for general agents}, author={Zuo, Yuxin and Xiao, Zikai and Sheng, Li and Huang, Fei and Tu, Jianhong and Liu, Yuxuan and Tang, Tianyi and Hu, Xiaomeng and Su, Yang and Lan, Qingfeng and others}, journal={arXiv preprint arXiv:2606.24597}, year={2026}}