Performance

AgentWorldBench (Open-Ended Evaluation)

Five-dimensional rubric mean per domain, normalized to 0-100 scale.

Quickstart

Deployment

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

SGLang is a fast serving framework for large language models.

python -m sglang.launch_server \
    --model-path Qwen/Qwen-AgentWorld-35B-A3B \
    --port 8000 \
    --tp-size 4 \
    --context-length 262144 \
    --reasoning-parser qwen3

An OpenAI-compatible API will be available at http://localhost:8000/v1.

vLLM

vLLM is a high-throughput and memory-efficient inference engine for LLMs.

vllm serve Qwen/Qwen-AgentWorld-35B-A3B \
    --port 8000 \
    --tensor-parallel-size 4 \
    --max-model-len 262144 \
    --reasoning-parser qwen3 \
    --trust-remote-code

An OpenAI-compatible API will be available at http://localhost:8000/v1.

Inference with Transformers

from 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)

Using via the Chat Completions API

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",
)

# Terminal domain example
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/"
    }
]

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)

[!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 a system_prompt.txt (world model system prompt) and a judge_system_prompt.txt (evaluation prompt).

Evaluate on AgentWorldBench

AgentWorldBench evaluates language world models by scoring each predicted environment observation on 5 dimensions: Format, Factuality, Consistency, Realism, and Quality.

Setup

# Clone the evaluation repository
git clone https://github.com/QwenLM/Qwen-AgentWorld.git
cd Qwen-AgentWorld

# Download the benchmark
huggingface-cli download Qwen/AgentWorldBench --repo-type dataset --local-dir ./AgentWorldBench

# Install dependencies
pip install openai

Run Evaluation

The evaluation follows a three-step pipeline:

cd eval

# Step 1: Run world model inference
python 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 scoring
export 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 scores
python eval.py score --predictions ./results/judged.jsonl

Best Practices

1. 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.

2. 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.

3. Domain-Specific System Prompts: For optimal simulation fidelity, use the domain-specific system prompts provided in the prompts/ directory of the GitHub repository.

Citation

If you find our work helpful, feel free to give us a cite.

@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}
}