---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen-AgentWorld-35B-A3B
datasets:
- Qwen/AgentWorldBench
tags:
- qwen
- unsloth
- world-model
- agent
- environment-simulation
---
# Qwen-AgentWorld-35B-A3B
> [!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 — CPT injects environment knowledge, SFT activates next-state-prediction reasoning, RL sharpens simulation fidelity — Qwen-AgentWorld is a **native world model**: environment modeling is the training objective from the CPT stage onward, not a post-hoc add-on.
## 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](https://huggingface.co/Qwen/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.
## Performance
### AgentWorldBench (Open-Ended Evaluation)
Five-dimensional rubric mean per domain, normalized to 0-100 scale.
| 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 | 35.14 | **59.18** | 64.10 | 61.50 | **54.66** | 66.62 | 56.59 |
| Claude Opus 4.6 | 69.90 | 29.30 | 57.51 | 64.55 | **61.74** | 51.42 | **70.20** | 57.80 |
| Gemini 3.1 Pro | 59.07 | 30.21 | 52.47 | 59.07 | 61.40 | 52.83 | 66.92 | 54.57 |
| Claude Sonnet 4.6 | 70.00 | 28.79 | 56.98 | 64.52 | 58.03 | 50.78 | 63.17 | 56.04 |
| DeepSeek-V4-Pro | 63.27 | 27.61 | 51.26 | 59.44 | 55.17 | 50.32 | 63.70 | 52.97 |
| GLM-5.1 | 67.60 | 22.46 | 47.32 | 52.07 | 59.10 | 51.50 | 59.13 | 51.31 |
| Kimi K2.6 | 65.23 | 27.48 | 52.54 | 58.77 | 58.93 | 50.20 | 60.80 | 53.42 |
| MiniMax-M2.7 | 55.82 | 27.30 | 41.62 | 37.44 | 52.40 | 50.52 | 57.73 | 46.12 |
| Qwen3.5-35B-A3B | 57.87 | 25.98 | 46.13 | 47.58 | 53.18 | 47.10 | 56.27 | 47.73 |
| Qwen3.5-397B-A17B | 68.31 | 30.81 | 55.30 | 64.44 | 54.90 | 48.55 | 60.85 | 54.74 |
| Qwen3.6-Plus | 55.28 | 21.94 | 50.58 | 59.08 | 57.65 | 50.78 | 60.33 | 50.81 |
| **Qwen-AgentWorld-35B-A3B** | 64.79 | 36.69 | 53.96 | 65.63 | 58.17 | 49.55 | 65.92 | 56.39 |
| **Qwen-AgentWorld-397B-A17B** | 68.24 | **37.82** | 57.73 | **68.49** | 60.20 | 50.98 | 67.89 | **58.71** |
## 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](https://github.com/sgl-project/sglang) is a fast serving framework for large language models.
```bash
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](https://github.com/vllm-project/vllm) is a high-throughput and memory-efficient inference engine for LLMs.
```bash
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
```python
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
```python
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/`](https://github.com/QwenLM/Qwen-AgentWorld/tree/master/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
```bash
# 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:
```bash
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 (`...`) 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/`](https://github.com/QwenLM/Qwen-AgentWorld/tree/master/prompts) directory of the GitHub repository.
## Citation
If you find our work helpful, feel free to give us a cite.
```bibtex
@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}
}
```
