unsloth

Qwen-AgentWorld-35B-A3B

Deploy Dedicated

README

License: apache-2.0

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.

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.

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 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 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)

[!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

bash

# 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 openai

Run Evaluation

The evaluation follows a three-step pipeline:

bash

cd 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

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.

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}}

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README

License: apache-2.0

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.

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.

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 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)

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 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)

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)

[!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

bash

# 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 openai

Run Evaluation

The evaluation follows a three-step pipeline:

bash

cd 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

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.

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}}