Highlights
- Seven Unified Domains. A single model covers MCP (tool calling), Search, Terminal, SWE (software engineering), Android, Web, and OS — spanning both text and GUI interaction environments.
- Native World Model. Environment modeling from CPT onward, not post-hoc adaptation on a general-purpose LLM.
- Generalizable, Scalable & Controllable Simulator. Zero-shot generalization to OOD environments (e.g., OpenClaw); controllable perturbations and fictional-world construction surpass real-environment training.
- Agent Foundation Model. LWM RL warm-up on single-turn, non-agentic trajectories transfers to multi-turn, tool-calling agentic tasks across 7 benchmarks, including 3 entirely out-of-domain.
Model Overview
- Type: Causal Language Model (Language World Model)
- Base Model: Qwen3.5-35B-A3B-Base
- Training Stage: Continual Pre-Training (CPT) → Supervised Fine-Tuning (SFT) → Reinforcement Learning (RL, GSPO)
- Number of Parameters: 35B in total and 3B activated
- Hidden Dimension: 2048
- Token Embedding: 248320 (Padded)
- Number of Layers: 40
- Hidden Layout: 10 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE))
- Gated DeltaNet:
- Number of Linear Attention Heads: 32 for V and 16 for QK
- Head Dimension: 128
- Gated Attention:
- Number of Attention Heads: 16 for Q and 2 for KV
- Head Dimension: 256
- Rotary Position Embedding Dimension: 64
- Mixture Of Experts
- Number of Experts: 256
- Number of Activated Experts: 8 Routed + 1 Shared
- Expert Intermediate Dimension: 512
- Context Length: 262,144 tokens
- Disclaimer: No outputs from external API services are included in the training pipeline.
AgentWorldBench (Open-Ended Evaluation)
Five-dimensional rubric mean per domain, normalized to 0-100 scale.
Table with columns: Model, MCP, Search, Term., SWE, Android, Web, OS, Overall| Model | MCP | Search | Term. | SWE | Android | Web | OS | Overall |
|---|
| GPT-5.4 | 70.10 | 37.26 | 53.69 | 66.29 | 60.00 | 51.80 | 68.58 | 58.25 |
| Claude Opus 4.8 | 54.93 |
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.
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",
)
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
-
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.
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}
}