---

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

---

<div>

<p style="margin-top: 0;margin-bottom: 0;">

<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>

</p>

<div style="display: flex; gap: 5px; align-items: center; ">

<a href="https://github.com/unslothai/unsloth/">

<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">

</a>

<a href="https://discord.gg/unsloth">

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<a href="https://docs.unsloth.ai/">

<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">

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Qwen-AgentWorld-35B-A3B

<div style="text-align: center">

<img width="400px" src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen-AgentWorld/logo.png">

<p>

<a href="http://arxiv.org/abs/2606.24597">📑 Technical Report</a> |

<a href="https://qwen.ai/blog?id=qwen-agentworld">📖 Blog</a> |

<a href="https://huggingface.co/collections/Qwen/qwen-agentworld">🤗 Hugging Face</a> |

<a href="https://modelscope.cn/collections/Qwen/Qwen-AgentWorld">🤖 ModelScope</a> |

<a href="https://github.com/QwenLM/Qwen-AgentWorld">💻 GitHub</a> |

<a href="https://qwen.ai/blog?id=qwen-agentworld#interactive-demo-interactive-demo">🖥️ Demo</a>

</p>

</div>

> [!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
• 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 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.

1. 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.

1. 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}
}